realizing semantic learning: Topics by Science.gov

Sample records for realizing semantic learning

Learning, Realizability and Games in Classical Arithmetic

NASA Astrophysics Data System (ADS)

Aschieri, Federico

2010-12-01

In this dissertation we provide mathematical evidence that the concept of learning can be used to give a new and intuitive computational semantics of classical proofs in various fragments of Predicative Arithmetic. First, we extend Kreisel modified realizability to a classical fragment of first order Arithmetic, Heyting Arithmetic plus EM1 (Excluded middle axiom restricted to Sigma^0_1 formulas). We introduce a new realizability semantics we call "Interactive Learning-Based Realizability". Our realizers are self-correcting programs, which learn from their errors and evolve through time. Secondly, we extend the class of learning based realizers to a classical version PCFclass of PCF and, then, compare the resulting notion of realizability with Coquand game semantics and prove a full soundness and completeness result. In particular, we show there is a one-to-one correspondence between realizers and recursive winning strategies in the 1-Backtracking version of Tarski games. Third, we provide a complete and fully detailed constructive analysis of learning as it arises in learning based realizability for HA+EM1, Avigad's update procedures and epsilon substitution method for Peano Arithmetic PA. We present new constructive techniques to bound the length of learning processes and we apply them to reprove - by means of our theory - the classic result of Godel that provably total functions of PA can be represented in Godel's system T. Last, we give an axiomatization of the kind of learning that is needed to computationally interpret Predicative classical second order Arithmetic. Our work is an extension of Avigad's and generalizes the concept of update procedure to the transfinite case. Transfinite update procedures have to learn values of transfinite sequences of non computable functions in order to extract witnesses from classical proofs.
A Research on E - learning Resources Construction Based on Semantic Web

NASA Astrophysics Data System (ADS)

Rui, Liu; Maode, Deng

Traditional e-learning platforms have the flaws that it's usually difficult to query or positioning, and realize the cross platform sharing and interoperability. In the paper, the semantic web and metadata standard is discussed, and a kind of e - learning system framework based on semantic web is put forward to try to solve the flaws of traditional elearning platforms.
The New Challenges for E-learning: The Educational Semantic Web

ERIC Educational Resources Information Center

Aroyo, Lora; Dicheva, Darina

2004-01-01

The big question for many researchers in the area of educational systems now is what is the next step in the evolution of e-learning? Are we finally moving from a scattered intelligence to a coherent space of collaborative intelligence? How close we are to the vision of the Educational Semantic Web and what do we need to do in order to realize it?…
Flexible Simulation E-Learning Environment for Studying Digital Circuits and Possibilities for It Deployment as Semantic Web Service

ERIC Educational Resources Information Center

Radoyska, P.; Ivanova, T.; Spasova, N.

2011-01-01

In this article we present a partially realized project for building a distributed learning environment for studying digital circuits Test and Diagnostics at TU-Sofia. We describe the main requirements for this environment, substantiate the developer platform choice, and present our simulation and circuit parameter calculation tools.…
Dynamic Influence of Emotional States on Novel Word Learning

PubMed Central

Guo, Jingjing; Zou, Tiantian; Peng, Danling

2018-01-01

Many researchers realize that it's unrealistic to isolate language learning and processing from emotions. However, few studies on language learning have taken emotions into consideration so far, so that the probable influences of emotions on language learning are unclear. The current study thereby aimed to examine the effects of emotional states on novel word learning and their dynamic changes with learning continuing and task varying. Positive, negative or neutral pictures were employed to induce a given emotional state, and then participants learned the novel words through association with line-drawing pictures in four successive learning phases. At the end of each learning phase, participants were instructed to fulfill a semantic category judgment task (in Experiment 1) or a word-picture semantic consistency judgment task (in Experiment 2) to explore the effects of emotional states on different depths of word learning. Converging results demonstrated that negative emotional state led to worse performance compared with neutral condition; however, how positive emotional state affected learning varied with learning task. Specifically, a facilitative role of positive emotional state in semantic category learning was observed but disappeared in word specific meaning learning. Moreover, the emotional modulation on novel word learning was quite dynamic and changeable with learning continuing, and the final attainment of the learned words tended to be similar under different emotional states. The findings suggest that the impact of emotion can be offset when novel words became more and more familiar and a part of existent lexicon. PMID:29695994
Dynamic Influence of Emotional States on Novel Word Learning.

PubMed

Guo, Jingjing; Zou, Tiantian; Peng, Danling

2018-01-01

Many researchers realize that it's unrealistic to isolate language learning and processing from emotions. However, few studies on language learning have taken emotions into consideration so far, so that the probable influences of emotions on language learning are unclear. The current study thereby aimed to examine the effects of emotional states on novel word learning and their dynamic changes with learning continuing and task varying. Positive, negative or neutral pictures were employed to induce a given emotional state, and then participants learned the novel words through association with line-drawing pictures in four successive learning phases. At the end of each learning phase, participants were instructed to fulfill a semantic category judgment task (in Experiment 1) or a word-picture semantic consistency judgment task (in Experiment 2) to explore the effects of emotional states on different depths of word learning. Converging results demonstrated that negative emotional state led to worse performance compared with neutral condition; however, how positive emotional state affected learning varied with learning task. Specifically, a facilitative role of positive emotional state in semantic category learning was observed but disappeared in word specific meaning learning. Moreover, the emotional modulation on novel word learning was quite dynamic and changeable with learning continuing, and the final attainment of the learned words tended to be similar under different emotional states. The findings suggest that the impact of emotion can be offset when novel words became more and more familiar and a part of existent lexicon.
A Generic Evaluation Model for Semantic Web Services

NASA Astrophysics Data System (ADS)

Shafiq, Omair

Semantic Web Services research has gained momentum over the last few Years and by now several realizations exist. They are being used in a number of industrial use-cases. Soon software developers will be expected to use this infrastructure to build their B2B applications requiring dynamic integration. However, there is still a lack of guidelines for the evaluation of tools developed to realize Semantic Web Services and applications built on top of them. In normal software engineering practice such guidelines can already be found for traditional component-based systems. Also some efforts are being made to build performance models for servicebased systems. Drawing on these related efforts in component-oriented and servicebased systems, we identified the need for a generic evaluation model for Semantic Web Services applicable to any realization. The generic evaluation model will help users and customers to orient their systems and solutions towards using Semantic Web Services. In this chapter, we have presented the requirements for the generic evaluation model for Semantic Web Services and further discussed the initial steps that we took to sketch such a model. Finally, we discuss related activities for evaluating semantic technologies.
Representations for Semantic Learning Webs: Semantic Web Technology in Learning Support

ERIC Educational Resources Information Center

Dzbor, M.; Stutt, A.; Motta, E.; Collins, T.

2007-01-01

Recent work on applying semantic technologies to learning has concentrated on providing novel means of accessing and making use of learning objects. However, this is unnecessarily limiting: semantic technologies will make it possible to develop a range of educational Semantic Web services, such as interpretation, structure-visualization, support…
Carving the World for Language: How Neuroscientific Research Can Enrich the Study of First and Second Language Learning

PubMed Central

George, Nathan R.; Göksun, Tilbe; Hirsh-Pasek, Kathy; Golinkoff, Roberta Michnick

2014-01-01

Linguistics, psychology, and neuroscience all have rich histories in language research. Crosstalk among these disciplines, as realized in studies of phonology, is pivotal for understanding a fundamental challenge for first and second language learners (SLLs): learning verbs. Linguistic and behavioral research with monolinguals suggests that infants attend to foundational event components (e.g., path, manner). Language then heightens or dampens attention to these components as children map word to world in language-specific ways. Cross-linguistic differences in semantic organization also reveal sources of struggles for SLLs. We discuss how better integrating neuroscience into this literature can unlock additional mysteries of verb learning. PMID:24854772
Carving the world for language: how neuroscientific research can enrich the study of first and second language learning.

PubMed

George, Nathan R; Göksun, Tilbe; Hirsh-Pasek, Kathy; Golinkoff, Roberta Michnick

2014-01-01

Linguistics, psychology, and neuroscience all have rich histories in language research. Crosstalk among these disciplines, as realized in studies of phonology, is pivotal for understanding a fundamental challenge for first and second language learners (SLLs): learning verbs. Linguistic and behavioral research with monolinguals suggests that infants attend to foundational event components (e.g., path, manner). Language then heightens or dampens attention to these components as children map word to world in language-specific ways. Cross-linguistic differences in semantic organization also reveal sources of struggles for SLLs. We discuss how better integrating neuroscience into this literature can unlock additional mysteries of verb learning.
Semantic-gap-oriented active learning for multilabel image annotation.

PubMed

Tang, Jinhui; Zha, Zheng-Jun; Tao, Dacheng; Chua, Tat-Seng

2012-04-01

User interaction is an effective way to handle the semantic gap problem in image annotation. To minimize user effort in the interactions, many active learning methods were proposed. These methods treat the semantic concepts individually or correlatively. However, they still neglect the key motivation of user feedback: to tackle the semantic gap. The size of the semantic gap of each concept is an important factor that affects the performance of user feedback. User should pay more efforts to the concepts with large semantic gaps, and vice versa. In this paper, we propose a semantic-gap-oriented active learning method, which incorporates the semantic gap measure into the information-minimization-based sample selection strategy. The basic learning model used in the active learning framework is an extended multilabel version of the sparse-graph-based semisupervised learning method that incorporates the semantic correlation. Extensive experiments conducted on two benchmark image data sets demonstrated the importance of bringing the semantic gap measure into the active learning process.
Semantic e-Learning: Next Generation of e-Learning?

NASA Astrophysics Data System (ADS)

Konstantinos, Markellos; Penelope, Markellou; Giannis, Koutsonikos; Aglaia, Liopa-Tsakalidi

Semantic e-learning aspires to be the next generation of e-learning, since the understanding of learning materials and knowledge semantics allows their advanced representation, manipulation, sharing, exchange and reuse and ultimately promote efficient online experiences for users. In this context, the paper firstly explores some fundamental Semantic Web technologies and then discusses current and potential applications of these technologies in e-learning domain, namely, Semantic portals, Semantic search, personalization, recommendation systems, social software and Web 2.0 tools. Finally, it highlights future research directions and open issues of the field.
Behavioral semantics of learning and crossmodal processing in auditory cortex: the semantic processor concept.

PubMed

Scheich, Henning; Brechmann, André; Brosch, Michael; Budinger, Eike; Ohl, Frank W; Selezneva, Elena; Stark, Holger; Tischmeyer, Wolfgang; Wetzel, Wolfram

2011-01-01

Two phenomena of auditory cortex activity have recently attracted attention, namely that the primary field can show different types of learning-related changes of sound representation and that during learning even this early auditory cortex is under strong multimodal influence. Based on neuronal recordings in animal auditory cortex during instrumental tasks, in this review we put forward the hypothesis that these two phenomena serve to derive the task-specific meaning of sounds by associative learning. To understand the implications of this tenet, it is helpful to realize how a behavioral meaning is usually derived for novel environmental sounds. For this purpose, associations with other sensory, e.g. visual, information are mandatory to develop a connection between a sound and its behaviorally relevant cause and/or the context of sound occurrence. This makes it plausible that in instrumental tasks various non-auditory sensory and procedural contingencies of sound generation become co-represented by neuronal firing in auditory cortex. Information related to reward or to avoidance of discomfort during task learning, that is essentially non-auditory, is also co-represented. The reinforcement influence points to the dopaminergic internal reward system, the local role of which for memory consolidation in auditory cortex is well-established. Thus, during a trial of task performance, the neuronal responses to the sounds are embedded in a sequence of representations of such non-auditory information. The embedded auditory responses show task-related modulations of auditory responses falling into types that correspond to three basic logical classifications that may be performed with a perceptual item, i.e. from simple detection to discrimination, and categorization. This hierarchy of classifications determine the semantic "same-different" relationships among sounds. Different cognitive classifications appear to be a consequence of learning task and lead to a recruitment of different excitatory and inhibitory mechanisms and to distinct spatiotemporal metrics of map activation to represent a sound. The described non-auditory firing and modulations of auditory responses suggest that auditory cortex, by collecting all necessary information, functions as a "semantic processor" deducing the task-specific meaning of sounds by learning. © 2010. Published by Elsevier B.V.
An Intelligent Semantic E-Learning Framework Using Context-Aware Semantic Web Technologies

ERIC Educational Resources Information Center

Huang, Weihong; Webster, David; Wood, Dawn; Ishaya, Tanko

2006-01-01

Recent developments of e-learning specifications such as Learning Object Metadata (LOM), Sharable Content Object Reference Model (SCORM), Learning Design and other pedagogy research in semantic e-learning have shown a trend of applying innovative computational techniques, especially Semantic Web technologies, to promote existing content-focused…
Semantic Categories and Context in L2 Vocabulary Learning

ERIC Educational Resources Information Center

Bolger, Patrick; Zapata, Gabriela

2011-01-01

This article extends recent findings that presenting semantically related vocabulary simultaneously inhibits learning. It does so by adding story contexts. Participants learned 32 new labels for known concepts from four different semantic categories in stories that were either semantically related (one category per story) or semantically unrelated…
Collaborative E-Learning Using Semantic Course Blog

ERIC Educational Resources Information Center

Lu, Lai-Chen; Yeh, Ching-Long

2008-01-01

Collaborative e-learning delivers many enhancements to e-learning technology; it enables students to collaborate with each other and improves their learning efficiency. Semantic blog combines semantic Web and blog technology that users can import, export, view, navigate, and query the blog. We developed a semantic course blog for collaborative…
Semantic Image Segmentation with Contextual Hierarchical Models.

PubMed

Seyedhosseini, Mojtaba; Tasdizen, Tolga

2016-05-01

Semantic segmentation is the problem of assigning an object label to each pixel. It unifies the image segmentation and object recognition problems. The importance of using contextual information in semantic segmentation frameworks has been widely realized in the field. We propose a contextual framework, called contextual hierarchical model (CHM), which learns contextual information in a hierarchical framework for semantic segmentation. At each level of the hierarchy, a classifier is trained based on downsampled input images and outputs of previous levels. Our model then incorporates the resulting multi-resolution contextual information into a classifier to segment the input image at original resolution. This training strategy allows for optimization of a joint posterior probability at multiple resolutions through the hierarchy. Contextual hierarchical model is purely based on the input image patches and does not make use of any fragments or shape examples. Hence, it is applicable to a variety of problems such as object segmentation and edge detection. We demonstrate that CHM performs at par with state-of-the-art on Stanford background and Weizmann horse datasets. It also outperforms state-of-the-art edge detection methods on NYU depth dataset and achieves state-of-the-art on Berkeley segmentation dataset (BSDS 500).
The Role of Simple Semantics in the Process of Artificial Grammar Learning.

PubMed

Öttl, Birgit; Jäger, Gerhard; Kaup, Barbara

2017-10-01

This study investigated the effect of semantic information on artificial grammar learning (AGL). Recursive grammars of different complexity levels (regular language, mirror language, copy language) were investigated in a series of AGL experiments. In the with-semantics condition, participants acquired semantic information prior to the AGL experiment; in the without-semantics control condition, participants did not receive semantic information. It was hypothesized that semantics would generally facilitate grammar acquisition and that the learning benefit in the with-semantics conditions would increase with increasing grammar complexity. Experiment 1 showed learning effects for all grammars but no performance difference between conditions. Experiment 2 replicated the absence of a semantic benefit for all grammars even though semantic information was more prominent during grammar acquisition as compared to Experiment 1. Thus, we did not find evidence for the idea that semantics facilitates grammar acquisition, which seems to support the view of an independent syntactic processing component.
Episodic representations support early semantic learning: evidence from midazolam induced amnesia.

PubMed

Merritt, Paul; Hirshman, Elliot; Zamani, Shane; Hsu, John; Berrigan, Michael

2006-07-01

Current controversy exists regarding the role of episodic representations in the formation of long-term semantic memories. Using the drug midazolam to induce temporary amnesia we tested participants' memories for newly learned facts in a semantic cue condition or an episodic and semantic cue condition. Following midazolam administration, memory performance was superior in the episodic and semantic condition, suggesting early semantic learning is supported by episodic representations.
Facilitation and interference in naming: A consequence of the same learning process?

PubMed

Hughes, Julie W; Schnur, Tatiana T

2017-08-01

Our success with naming depends on what we have named previously, a phenomenon thought to reflect learning processes. Repeatedly producing the same name facilitates language production (i.e., repetition priming), whereas producing semantically related names hinders subsequent performance (i.e., semantic interference). Semantic interference is found whether naming categorically related items once (continuous naming) or multiple times (blocked cyclic naming). A computational model suggests that the same learning mechanism responsible for facilitation in repetition creates semantic interference in categorical naming (Oppenheim, Dell, & Schwartz, 2010). Accordingly, we tested the predictions that variability in semantic interference is correlated across categorical naming tasks and is caused by learning, as measured by two repetition priming tasks (picture-picture repetition priming, Exp. 1; definition-picture repetition priming, Exp. 2, e.g., Wheeldon & Monsell, 1992). In Experiment 1 (77 subjects) semantic interference and repetition priming effects were robust, but the results revealed no relationship between semantic interference effects across contexts. Critically, learning (picture-picture repetition priming) did not predict semantic interference effects in either task. We replicated these results in Experiment 2 (81 subjects), finding no relationship between semantic interference effects across tasks or between semantic interference effects and learning (definition-picture repetition priming). We conclude that the changes underlying facilitatory and interfering effects inherent to lexical access are the result of distinct learning processes where multiple mechanisms contribute to semantic interference in naming. Copyright © 2017 Elsevier B.V. All rights reserved.

Semantic Richness and Word Learning in Children with Autism Spectrum Disorder

ERIC Educational Resources Information Center

Gladfelter, Allison; Goffman, Lisa

2018-01-01

Semantically rich learning contexts facilitate semantic, phonological, and articulatory aspects of word learning in children with typical development (TD). However, because children with autism spectrum disorder (ASD) show differences at each of these processing levels, it is unclear whether they will benefit from semantic cues in the same manner…
A Learning Content Authoring Approach Based on Semantic Technologies and Social Networking: An Empirical Study

ERIC Educational Resources Information Center

Nesic, Sasa; Gasevic, Dragan; Jazayeri, Mehdi; Landoni, Monica

2011-01-01

Semantic web technologies have been applied to many aspects of learning content authoring including semantic annotation, semantic search, dynamic assembly, and personalization of learning content. At the same time, social networking services have started to play an important role in the authoring process by supporting authors' collaborative…
Semantics vs Pragmatics of a Compound Word

ERIC Educational Resources Information Center

Smirnova, Elena A.; Biktemirova, Ella I.; Davletbaeva, Diana N.

2016-01-01

This paper is devoted to the study of correlation between semantic and pragmatic potential of a compound word, which functions in informal speech, and the mechanisms of secondary nomination, which realizes the potential of semantic-pragmatic features of colloquial compounds. The relevance and the choice of the research question is based on the…
The Semantic Learning Organization

ERIC Educational Resources Information Center

Sicilia, Miguel-Angel; Lytras, Miltiadis D.

2005-01-01

Purpose: The aim of this paper is introducing the concept of a "semantic learning organization" (SLO) as an extension of the concept of "learning organization" in the technological domain. Design/methodology/approach: The paper takes existing definitions and conceptualizations of both learning organizations and Semantic Web technology to develop…
Long-lasting perceptual priming and semantic learning in amnesia: a case experiment.

PubMed

Tulving, E; Hayman, C A; Macdonald, C A

1991-07-01

An investigation of perceptual priming and semantic learning in the severely amnesic subject K.C. is reported. He was taught 64 three-word sentences and tested for his ability to produce the final word of each sentence. Despite a total lack of episodic memory, he exhibited (a) strong perceptual priming effects in word-fragment completion, which were retained essentially in full strength for 12 months, and (b) independent of perceptual priming, learning of new semantic facts, many of which were also retained for 12 months. K.C.'s semantic learning may be at least partly attributable to repeated study trials and minimal interference during learning. The findings suggest that perceptual priming and semantic learning are subserved by two memory systems different from episodic memory and that both systems (perceptual representation and semantic memory) are at least partially preserved in some amnesic subjects.
Implicit Learning of Semantic Preferences of Verbs

ERIC Educational Resources Information Center

Paciorek, Albertyna; Williams, John N.

2015-01-01

Previous studies of semantic implicit learning in language have only examined learning grammatical form-meaning connections in which learning could have been supported by prior linguistic knowledge. In this study we target the domain of verb meaning, specifically semantic preferences regarding novel verbs (e.g., the preference for a novel verb to…
ELE: An Ontology-Based System Integrating Semantic Search and E-Learning Technologies

ERIC Educational Resources Information Center

Barbagallo, A.; Formica, A.

2017-01-01

ELSE (E-Learning for the Semantic ECM) is an ontology-based system which integrates semantic search methodologies and e-learning technologies. It has been developed within a project of the CME (Continuing Medical Education) program--ECM (Educazione Continua nella Medicina) for Italian participants. ELSE allows the creation of e-learning courses…
Semantic Coherence Facilitates Distributional Learning.

PubMed

Ouyang, Long; Boroditsky, Lera; Frank, Michael C

2017-04-01

Computational models have shown that purely statistical knowledge about words' linguistic contexts is sufficient to learn many properties of words, including syntactic and semantic category. For example, models can infer that "postman" and "mailman" are semantically similar because they have quantitatively similar patterns of association with other words (e.g., they both tend to occur with words like "deliver," "truck," "package"). In contrast to these computational results, artificial language learning experiments suggest that distributional statistics alone do not facilitate learning of linguistic categories. However, experiments in this paradigm expose participants to entirely novel words, whereas real language learners encounter input that contains some known words that are semantically organized. In three experiments, we show that (a) the presence of familiar semantic reference points facilitates distributional learning and (b) this effect crucially depends both on the presence of known words and the adherence of these known words to some semantic organization. Copyright © 2016 Cognitive Science Society, Inc.
Designing Collaborative E-Learning Environments Based upon Semantic Wiki: From Design Models to Application Scenarios

ERIC Educational Resources Information Center

Li, Yanyan; Dong, Mingkai; Huang, Ronghuai

2011-01-01

The knowledge society requires life-long learning and flexible learning environment that enables fast, just-in-time and relevant learning, aiding the development of communities of knowledge, linking learners and practitioners with experts. Based upon semantic wiki, a combination of wiki and Semantic Web technology, this paper designs and develops…
The Role of Simple Semantics in the Process of Artificial Grammar Learning

ERIC Educational Resources Information Center

Öttl, Birgit; Jäger, Gerhard; Kaup, Barbara

2017-01-01

This study investigated the effect of semantic information on artificial grammar learning (AGL). Recursive grammars of different complexity levels (regular language, mirror language, copy language) were investigated in a series of AGL experiments. In the with-semantics condition, participants acquired semantic information prior to the AGL…
Intelligent Learning Infrastructure for Knowledge Intensive Organizations: A Semantic Web Perspective

ERIC Educational Resources Information Center

Lytras, Miltiadis, Ed.; Naeve, Ambjorn, Ed.

2005-01-01

In the context of Knowledge Society, the convergence of knowledge and learning management is a critical milestone. "Intelligent Learning Infrastructure for Knowledge Intensive Organizations: A Semantic Web Perspective" provides state-of-the art knowledge through a balanced theoretical and technological discussion. The semantic web perspective…
Semantic-Aware Components and Services of ActiveMath

ERIC Educational Resources Information Center

Melis, Erica; Goguadze, Giorgi; Homik, Martin; Libbrecht, Paul; Ullrich, Carsten; Winterstein, Stefan

2006-01-01

ActiveMath is a complex web-based adaptive learning environment with a number of components and interactive learning tools. The basis for handling semantics of learning content is provided by its semantic (mathematics) content markup, which is additionally annotated with educational metadata. Several components, tools and external services can…
Automatic event recognition and anomaly detection with attribute grammar by learning scene semantics

NASA Astrophysics Data System (ADS)

Qi, Lin; Yao, Zhenyu; Li, Li; Dong, Junyu

2007-11-01

In this paper we present a novel framework for automatic event recognition and abnormal behavior detection with attribute grammar by learning scene semantics. This framework combines learning scene semantics by trajectory analysis and constructing attribute grammar-based event representation. The scene and event information is learned automatically. Abnormal behaviors that disobey scene semantics or event grammars rules are detected. By this method, an approach to understanding video scenes is achieved. Further more, with this prior knowledge, the accuracy of abnormal event detection is increased.
SOLE: Applying Semantics and Social Web to Support Technology Enhanced Learning in Software Engineering

NASA Astrophysics Data System (ADS)

Colomo-Palacios, Ricardo; Jiménez-López, Diego; García-Crespo, Ángel; Blanco-Iglesias, Borja

eLearning educative processes are a challenge for educative institutions and education professionals. In an environment in which learning resources are being produced, catalogued and stored using innovative ways, SOLE provides a platform in which exam questions can be produced supported by Web 2.0 tools, catalogued and labeled via semantic web and stored and distributed using eLearning standards. This paper presents, SOLE, a social network of exam questions sharing particularized for Software Engineering domain, based on semantics and built using semantic web and eLearning standards, such as IMS Question and Test Interoperability specification 2.1.
Episodic Representations Support Early Semantic Learning: Evidence from Midazolam Induced Amnesia

ERIC Educational Resources Information Center

Merritt, Paul; Hirshman, Elliot; Zamani, Shane; Hsu, John; Berrigan, Michael

2006-01-01

Current controversy exists regarding the role of episodic representations in the formation of long-term semantic memories. Using the drug "midazolam" to induce temporary amnesia we tested participants' memories for newly learned facts in a semantic cue condition or an episodic and semantic cue condition. Following midazolam administration, memory…
Children and adolescents' performance on a medium-length/nonsemantic word-list test.

PubMed

Flores-Lázaro, Julio César; Salgado Soruco, María Alejandra; Stepanov, Igor I

2017-01-01

Word-list learning tasks are among the most important and frequently used tests for declarative memory evaluation. For example, the California Verbal Learning Test-Children's Version (CVLT-C) and Rey Auditory Verbal Learning Test provide important information about different cognitive-neuropsychological processes. However, the impact of test length (i.e., number of words) and semantic organization (i.e., type of words) on children's and adolescents' memory performance remains to be clarified, especially during this developmental stage. To explore whether a medium-length non-semantically organized test can produce the typical curvilinear performance that semantically organized tests produce, reflecting executive control, we studied and compared the cognitive performance of normal children and adolescents by utilizing mathematical modeling. The model is based on the first-order system transfer function and has been successfully applied to learning curves for the CVLT-C (15 words, semantically organized paradigm). Results indicate that learning nine semantically unrelated words produces typical curvilinear (executive function) performance in children and younger adolescents and that performance could be effectively analyzed with the mathematical model. This indicates that the exponential increase (curvilinear performance) of correctly learned words does not solely depend on semantic and/or length features. This type of test controls semantic and length effects and may represent complementary tools for executive function evaluation in clinical populations in which semantic and/or length processing are affected.
Hypermedia-Assisted Instruction and Second Language Learning: A Semantic-Network-Based Approach.

ERIC Educational Resources Information Center

Liu, Min

This literature review examines a hypermedia learning environment from a semantic network basis and the application of such an environment to second language learning. (A semantic network is defined as a conceptual representation of knowledge in human memory). The discussion is organized under the following headings and subheadings: (1) Advantages…
Effects of Interactive Vocabulary Instruction on the Vocabulary Learning and Reading Comprehension of Junior-High Learning Disabled Students.

ERIC Educational Resources Information Center

Bos, Candace S.; Anders, Patricia L.

1990-01-01

The study, involving 61 learning-disabled junior high students, compared the short-term and long-term effectiveness of definition instruction with interactive vocabulary strategies (semantic mapping, semantic feature analysis, and semantic/syntactic feature analysis). Students participating in the interactive strategies demonstrated greater…
Implicit Word Learning Benefits from Semantic Richness: Electrophysiological and Behavioral Evidence

ERIC Educational Resources Information Center

Rabovsky, Milena; Sommer, Werner; Abdel Rahman, Rasha

2012-01-01

Words differ considerably in the amount of associated semantic information. Despite the crucial role of meaning in language, it is still unclear whether and how this variability modulates language learning. Here, we provide initial evidence demonstrating that implicit learning in repetition priming is influenced by the amount of semantic features…
Web 3.0: Implications for Online Learning

ERIC Educational Resources Information Center

Morris, Robin D.

2010-01-01

The impact of Web 3.0, also known as the Semantic Web, on online learning is yet to be determined as the Semantic Web and its technologies continue to develop. Online instructors must have a rudimentary understanding of Web 3.0 to prepare for the next phase of online learning. This paper provides an understandable definition of the Semantic Web…

Biologically Inspired Model for Visual Cognition Achieving Unsupervised Episodic and Semantic Feature Learning.

PubMed

Qiao, Hong; Li, Yinlin; Li, Fengfu; Xi, Xuanyang; Wu, Wei

2016-10-01

Recently, many biologically inspired visual computational models have been proposed. The design of these models follows the related biological mechanisms and structures, and these models provide new solutions for visual recognition tasks. In this paper, based on the recent biological evidence, we propose a framework to mimic the active and dynamic learning and recognition process of the primate visual cortex. From principle point of view, the main contributions are that the framework can achieve unsupervised learning of episodic features (including key components and their spatial relations) and semantic features (semantic descriptions of the key components), which support higher level cognition of an object. From performance point of view, the advantages of the framework are as follows: 1) learning episodic features without supervision-for a class of objects without a prior knowledge, the key components, their spatial relations and cover regions can be learned automatically through a deep neural network (DNN); 2) learning semantic features based on episodic features-within the cover regions of the key components, the semantic geometrical values of these components can be computed based on contour detection; 3) forming the general knowledge of a class of objects-the general knowledge of a class of objects can be formed, mainly including the key components, their spatial relations and average semantic values, which is a concise description of the class; and 4) achieving higher level cognition and dynamic updating-for a test image, the model can achieve classification and subclass semantic descriptions. And the test samples with high confidence are selected to dynamically update the whole model. Experiments are conducted on face images, and a good performance is achieved in each layer of the DNN and the semantic description learning process. Furthermore, the model can be generalized to recognition tasks of other objects with learning ability.
Functional imaging of the semantic system: retrieval of sensory-experienced and verbally learned knowledge.

PubMed

Noppeney, Uta; Price, Cathy J

2003-01-01

This paper considers how functional neuro-imaging can be used to investigate the organization of the semantic system and the limitations associated with this technique. The majority of the functional imaging studies of the semantic system have looked for divisions by varying stimulus category. These studies have led to divergent results and no clear anatomical hypotheses have emerged to account for the dissociations seen in behavioral studies. Only a few functional imaging studies have used task as a variable to differentiate the neural correlates of semantic features more directly. We extend these findings by presenting a new study that contrasts tasks that differentially weight sensory (color and taste) and verbally learned (origin) semantic features. Irrespective of the type of semantic feature retrieved, a common semantic system was activated as demonstrated in many previous studies. In addition, the retrieval of verbally learned, but not sensory-experienced, features enhanced activation in medial and lateral posterior parietal areas. We attribute these "verbally learned" effects to differences in retrieval strategy and conclude that evidence for segregation of semantic features at an anatomical level remains weak. We believe that functional imaging has the potential to increase our understanding of the neuronal infrastructure that sustains semantic processing but progress may require multiple experiments until a consistent explanatory framework emerges.
The Analysis of RDF Semantic Data Storage Optimization in Large Data Era

NASA Astrophysics Data System (ADS)

He, Dandan; Wang, Lijuan; Wang, Can

2018-03-01

With the continuous development of information technology and network technology in China, the Internet has also ushered in the era of large data. In order to obtain the effective acquisition of information in the era of large data, it is necessary to optimize the existing RDF semantic data storage and realize the effective query of various data. This paper discusses the storage optimization of RDF semantic data under large data.
Meaningful Memory in Acute Anorexia Nervosa Patients-Comparing Recall, Learning, and Recognition of Semantically Related and Semantically Unrelated Word Stimuli.

PubMed

Terhoeven, Valentin; Kallen, Ursula; Ingenerf, Katrin; Aschenbrenner, Steffen; Weisbrod, Matthias; Herzog, Wolfgang; Brockmeyer, Timo; Friederich, Hans-Christoph; Nikendei, Christoph

2017-03-01

It is unclear whether observed memory impairment in anorexia nervosa (AN) depends on the semantic structure (categorized words) of material to be encoded. We aimed to investigate the processing of semantically related information in AN. Memory performance was assessed in a recall, learning, and recognition test in 27 adult women with AN (19 restricting, 8 binge-eating/purging subtype; average disease duration: 9.32 years) and 30 healthy controls using an extended version of the Rey Auditory Verbal Learning Test, applying semantically related and unrelated word stimuli. Short-term memory (immediate recall, learning), regardless of semantics of the words, was significantly worse in AN patients, whereas long-term memory (delayed recall, recognition) did not differ between AN patients and controls. Semantics of stimuli do not have a better effect on memory recall in AN compared to CO. Impaired short-term versus long-term memory is discussed in relation to dysfunctional working memory in AN. Copyright © 2016 John Wiley & Sons, Ltd and Eating Disorders Association. Copyright © 2016 John Wiley & Sons, Ltd and Eating Disorders Association.
Effects of Embedded Processing Tasks on Learning Outcomes.

ERIC Educational Resources Information Center

Hobbs, D. J.

1987-01-01

Describes a British study with undergraduate accountancy students which compared the quantitative and qualitative effects of three types of embedded tasks or questions--relational-semantic, transpose-semantic, and non-semantic--on learning outcomes. Variables investigated included mathematical background, recall, and comprehension. Relevance of…
Semantic Interoperability Almost Without Using The Same Vocabulary: Is It Possible?

NASA Astrophysics Data System (ADS)

Krisnadhi, A. A.

2016-12-01

Semantic interoperability, which is a key requirement in realizing cross-repository data integration, is often understood as using the same ontology or vocabulary. Consequently, within a particular domain, one can easily assume that there has to be one unifying domain ontology covering as many vocabulary terms in the domain as possible in order to realize any form of data integration across multiple data sources. Furthermore, the desire to provide very precise definition of those many terms led to the development of huge, foundational and domain ontologies that are comprehensive, but too complicated, restrictive, monolithic, and difficult to use and reuse, which cause common data providers to avoid using them. This problem is especially true in a domain as diverse as geosciences as it is virtually impossible to reach an agreement to the semantics of many terms (e.g., there are hundreds of definitions of forest used throughout the world). To overcome this challenge, modular ontology architecture has emerged in recent years, fueled among others, by advances in the ontology design pattern research. Each ontology pattern models only one key notion. It can act as a small module of a larger ontology. Such a module is developed in such a way that it is largely independent of how other notions in the same domain are modeled. This leads to an increased reusability. Furthermore, an ontology formed out of such modules would have an improved understandability over large, monolithic ontologies. Semantic interoperability in the aforementioned architecture is not achieved by enforcing the use of the same vocabulary, but rather, promoting alignment to the same ontology patterns. In this work, we elaborate how this architecture realizes the above idea. In particular, we describe how multiple data sources with differing perspectives and vocabularies can interoperate through this architecture. Building the solution upon semantic technologies such as Linked Data and the Web Ontology Language (OWL), we demonstrate how a data integration solution based on this idea can be realized over different data repositories.
Effect of episodic and working memory impairments on semantic and cognitive procedural learning at alcohol treatment entry.

PubMed

Pitel, Anne Lise; Witkowski, Thomas; Vabret, François; Guillery-Girard, Bérengère; Desgranges, Béatrice; Eustache, Francis; Beaunieux, Hélène

2007-02-01

Chronic alcoholism is known to impair the functioning of episodic and working memory, which may consequently reduce the ability to learn complex novel information. Nevertheless, semantic and cognitive procedural learning have not been properly explored at alcohol treatment entry, despite its potential clinical relevance. The goal of the present study was therefore to determine whether alcoholic patients, immediately after the weaning phase, are cognitively able to acquire complex new knowledge, given their episodic and working memory deficits. Twenty alcoholic inpatients with episodic memory and working memory deficits at alcohol treatment entry and a control group of 20 healthy subjects underwent a protocol of semantic acquisition and cognitive procedural learning. The semantic learning task consisted of the acquisition of 10 novel concepts, while subjects were administered the Tower of Toronto task to measure cognitive procedural learning. Analyses showed that although alcoholic subjects were able to acquire the category and features of the semantic concepts, albeit slowly, they presented impaired label learning. In the control group, executive functions and episodic memory predicted semantic learning in the first and second halves of the protocol, respectively. In addition to the cognitive processes involved in the learning strategies invoked by controls, alcoholic subjects seem to attempt to compensate for their impaired cognitive functions, invoking capacities of short-term passive storage. Regarding cognitive procedural learning, although the patients eventually achieved the same results as the controls, they failed to automate the procedure. Contrary to the control group, the alcoholic groups' learning performance was predicted by controlled cognitive functions throughout the protocol. At alcohol treatment entry, alcoholic patients with neuropsychological deficits have difficulty acquiring novel semantic and cognitive procedural knowledge. Compared with controls, they seem to use more costly learning strategies, which are nonetheless less efficient. These learning disabilities need to be considered when treatment requiring the acquisition of complex novel information is envisaged.
Finiteness in Jordanian Arabic: A Semantic and Morphosyntactic Approach

ERIC Educational Resources Information Center

Al-Aqarbeh, Rania

2011-01-01

Previous research on finiteness has been dominated by the studies in tensed languages, e.g. English. Consequently, finiteness has been identified with tense. The traditional definition influences the morphological, semantic, and syntactic characterization of finiteness which has also been equated with tense and its realization. The present study…
Workspaces in the Semantic Web

NASA Technical Reports Server (NTRS)

Wolfe, Shawn R.; Keller, RIchard M.

2005-01-01

Due to the recency and relatively limited adoption of Semantic Web technologies. practical issues related to technology scaling have received less attention than foundational issues. Nonetheless, these issues must be addressed if the Semantic Web is to realize its full potential. In particular, we concentrate on the lack of scoping methods that reduce the size of semantic information spaces so they are more efficient to work with and more relevant to an agent's needs. We provide some intuition to motivate the need for such reduced information spaces, called workspaces, give a formal definition, and suggest possible methods of deriving them.
Getting connected: Both associative and semantic links structure semantic memory for newly learned persons.

PubMed

Wiese, Holger; Schweinberger, Stefan R

2015-01-01

The present study examined whether semantic memory for newly learned people is structured by visual co-occurrence, shared semantics, or both. Participants were trained with pairs of simultaneously presented (i.e., co-occurring) preexperimentally unfamiliar faces, which either did or did not share additionally provided semantic information (occupation, place of living, etc.). Semantic information could also be shared between faces that did not co-occur. A subsequent priming experiment revealed faster responses for both co-occurrence/no shared semantics and no co-occurrence/shared semantics conditions, than for an unrelated condition. Strikingly, priming was strongest in the co-occurrence/shared semantics condition, suggesting additive effects of these factors. Additional analysis of event-related brain potentials yielded priming in the N400 component only for combined effects of visual co-occurrence and shared semantics, with more positive amplitudes in this than in the unrelated condition. Overall, these findings suggest that both semantic relatedness and visual co-occurrence are important when novel information is integrated into person-related semantic memory.
Decoding the Formation of New Semantics: MVPA Investigation of Rapid Neocortical Plasticity during Associative Encoding through Fast Mapping.

PubMed

Atir-Sharon, Tali; Gilboa, Asaf; Hazan, Hananel; Koilis, Ester; Manevitz, Larry M

2015-01-01

Neocortical structures typically only support slow acquisition of declarative memory; however, learning through fast mapping may facilitate rapid learning-induced cortical plasticity and hippocampal-independent integration of novel associations into existing semantic networks. During fast mapping the meaning of new words and concepts is inferred, and durable novel associations are incidentally formed, a process thought to support early childhood's exuberant learning. The anterior temporal lobe, a cortical semantic memory hub, may critically support such learning. We investigated encoding of semantic associations through fast mapping using fMRI and multivoxel pattern analysis. Subsequent memory performance following fast mapping was more efficiently predicted using anterior temporal lobe than hippocampal voxels, while standard explicit encoding was best predicted by hippocampal activity. Searchlight algorithms revealed additional activity patterns that predicted successful fast mapping semantic learning located in lateral occipitotemporal and parietotemporal neocortex and ventrolateral prefrontal cortex. By contrast, successful explicit encoding could be classified by activity in medial and dorsolateral prefrontal and parahippocampal cortices. We propose that fast mapping promotes incidental rapid integration of new associations into existing neocortical semantic networks by activating related, nonoverlapping conceptual knowledge. In healthy adults, this is better captured by unique anterior and lateral temporal lobe activity patterns, while hippocampal involvement is less predictive of this kind of learning.
Designing learning management system interoperability in semantic web

NASA Astrophysics Data System (ADS)

Anistyasari, Y.; Sarno, R.; Rochmawati, N.

2018-01-01

The extensive adoption of learning management system (LMS) has set the focus on the interoperability requirement. Interoperability is the ability of different computer systems, applications or services to communicate, share and exchange data, information, and knowledge in a precise, effective and consistent way. Semantic web technology and the use of ontologies are able to provide the required computational semantics and interoperability for the automation of tasks in LMS. The purpose of this study is to design learning management system interoperability in the semantic web which currently has not been investigated deeply. Moodle is utilized to design the interoperability. Several database tables of Moodle are enhanced and some features are added. The semantic web interoperability is provided by exploited ontology in content materials. The ontology is further utilized as a searching tool to match user’s queries and available courses. It is concluded that LMS interoperability in Semantic Web is possible to be performed.
E-Learning System Overview Based on Semantic Web

ERIC Educational Resources Information Center

Alsultanny, Yas A.

2006-01-01

The challenge of the semantic web is the provision of distributed information with well-defined meaning, understandable for different parties. e-Learning is efficient task relevant and just-in-time learning grown from the learning requirements of the new dynamically changing, distributed business world. In this paper we design an e-Learning system…
Challenges Facing the Semantic Web and Social Software as Communication Technology Agents in E-Learning Environments

ERIC Educational Resources Information Center

Olaniran, Bolanle A.

2010-01-01

The semantic web describes the process whereby information content is made available for machine consumption. With increased reliance on information communication technologies, the semantic web promises effective and efficient information acquisition and dissemination of products and services in the global economy, in particular, e-learning.…
Differential pattern of semantic memory organization between bipolar I and II disorders.

PubMed

Chang, Jae Seung; Choi, Sungwon; Ha, Kyooseob; Ha, Tae Hyon; Cho, Hyun Sang; Choi, Jung Eun; Cha, Boseok; Moon, Eunsoo

2011-06-01

Semantic cognition is one of the key factors in psychosocial functioning. The aim of this study was to explore the differences in pattern of semantic memory organization between euthymic patients with bipolar I and II disorders using the category fluency task. Study participants included 23 euthymic subjects with bipolar I disorder, 23 matched euthymic subjects with bipolar II disorder and 23 matched control subjects. All participants were assessed for verbal learning, recall, learning strategies, and fluency. The combined methods of hierarchical clustering and multidimensional scaling were used to compare the pattern of semantic memory organization among the three groups. Quantitative measures of verbal learning, recall, learning strategies, and fluency did not differ between the three groups. A two-cluster structure of semantic memory organization was identified for the three groups. Semantic structure was more disorganized in the bipolar I disorder group compared to the bipolar II disorder. In addition, patients with bipolar II disorder used less elaborate strategies of semantic memory organization than those of controls. Compared to healthy controls, strategies for categorization in semantic memory appear to be less knowledge-based in patients with bipolar disorders. A differential pattern of semantic memory organization between bipolar I and II disorders indicates a higher risk of cognitive abnormalities in patients with bipolar I disorder compared to patients with bipolar II disorder. Exploring qualitative nature of neuropsychological domains may provide an explanatory insight into the characteristic behaviors of patients with bipolar disorders. Copyright © 2011 Elsevier Inc. All rights reserved.
E-Learning for Depth in the Semantic Web

ERIC Educational Resources Information Center

Shafrir, Uri; Etkind, Masha

2006-01-01

In this paper, we describe concept parsing algorithms, a novel semantic analysis methodology at the core of a new pedagogy that focuses learners attention on deep comprehension of the conceptual content of learned material. Two new e-learning tools are described in some detail: interactive concept discovery learning and meaning equivalence…
Semantic Learning Modifies Perceptual Face Processing

ERIC Educational Resources Information Center

Heisz, Jennifer J.; Shedden, Judith M.

2009-01-01

Face processing changes when a face is learned with personally relevant information. In a five-day learning paradigm, faces were presented with rich semantic stories that conveyed personal information about the faces. Event-related potentials were recorded before and after learning during a passive viewing task. When faces were novel, we observed…
Learning semantic and visual similarity for endomicroscopy video retrieval.

PubMed

Andre, Barbara; Vercauteren, Tom; Buchner, Anna M; Wallace, Michael B; Ayache, Nicholas

2012-06-01

Content-based image retrieval (CBIR) is a valuable computer vision technique which is increasingly being applied in the medical community for diagnosis support. However, traditional CBIR systems only deliver visual outputs, i.e., images having a similar appearance to the query, which is not directly interpretable by the physicians. Our objective is to provide a system for endomicroscopy video retrieval which delivers both visual and semantic outputs that are consistent with each other. In a previous study, we developed an adapted bag-of-visual-words method for endomicroscopy retrieval, called "Dense-Sift," that computes a visual signature for each video. In this paper, we present a novel approach to complement visual similarity learning with semantic knowledge extraction, in the field of in vivo endomicroscopy. We first leverage a semantic ground truth based on eight binary concepts, in order to transform these visual signatures into semantic signatures that reflect how much the presence of each semantic concept is expressed by the visual words describing the videos. Using cross-validation, we demonstrate that, in terms of semantic detection, our intuitive Fisher-based method transforming visual-word histograms into semantic estimations outperforms support vector machine (SVM) methods with statistical significance. In a second step, we propose to improve retrieval relevance by learning an adjusted similarity distance from a perceived similarity ground truth. As a result, our distance learning method allows to statistically improve the correlation with the perceived similarity. We also demonstrate that, in terms of perceived similarity, the recall performance of the semantic signatures is close to that of visual signatures and significantly better than those of several state-of-the-art CBIR methods. The semantic signatures are thus able to communicate high-level medical knowledge while being consistent with the low-level visual signatures and much shorter than them. In our resulting retrieval system, we decide to use visual signatures for perceived similarity learning and retrieval, and semantic signatures for the output of an additional information, expressed in the endoscopist own language, which provides a relevant semantic translation of the visual retrieval outputs.
Aspects of Theme in the Method and Discussion Sections of Biology Journal Articles in English.

ERIC Educational Resources Information Center

Martinez, Iliana A.

2003-01-01

Analyzes the thematic structure of the method and Discussion section of biology research articles. A corpus of 30 journal articles was analyzed using the categories of systematic functional linguistics and a semantic categorization for unmarked themes realized by subject. Revealed differences in the semantic construction of the sections. (VWL)
English Orthographic Learning in Chinese-L1 Young EFL Beginners.

PubMed

Cheng, Yu-Lin

2017-12-01

English orthographic learning, among Chinese-L1 children who were beginning to learn English as a foreign language, was documented when: (1) only visual memory was at their disposal, (2) visual memory and either some letter-sound knowledge or some semantic information was available, and (3) visual memory, some letter-sound knowledge and some semantic information were all available. When only visual memory was available, orthographic learning (measured via an orthographic choice test) was meagre. Orthographic learning was significant when either semantic information or letter-sound knowledge supplemented visual memory, with letter-sound knowledge generating greater significance. Although the results suggest that letter-sound knowledge plays a more important role than semantic information, letter-sound knowledge alone does not suffice to achieve perfect orthographic learning, as orthographic learning was greatest when letter-sound knowledge and semantic information were both available. The present findings are congruent with a view that the orthography of a foreign language drives its orthographic learning more than L1 orthographic learning experience, thus extending Share's (Cognition 55:151-218, 1995) self-teaching hypothesis to include non-alphabetic L1 children's orthographic learning of an alphabetic foreign language. The little letter-sound knowledge development observed in the experiment-I control group indicates that very little letter-sound knowledge develops in the absence of dedicated letter-sound training. Given the important role of letter-sound knowledge in English orthographic learning, dedicated letter-sound instruction is highly recommended.

Structured Semantic Knowledge Can Emerge Automatically from Predicting Word Sequences in Child-Directed Speech

PubMed Central

Huebner, Philip A.; Willits, Jon A.

2018-01-01

Previous research has suggested that distributional learning mechanisms may contribute to the acquisition of semantic knowledge. However, distributional learning mechanisms, statistical learning, and contemporary “deep learning” approaches have been criticized for being incapable of learning the kind of abstract and structured knowledge that many think is required for acquisition of semantic knowledge. In this paper, we show that recurrent neural networks, trained on noisy naturalistic speech to children, do in fact learn what appears to be abstract and structured knowledge. We trained two types of recurrent neural networks (Simple Recurrent Network, and Long Short-Term Memory) to predict word sequences in a 5-million-word corpus of speech directed to children ages 0–3 years old, and assessed what semantic knowledge they acquired. We found that learned internal representations are encoding various abstract grammatical and semantic features that are useful for predicting word sequences. Assessing the organization of semantic knowledge in terms of the similarity structure, we found evidence of emergent categorical and hierarchical structure in both models. We found that the Long Short-term Memory (LSTM) and SRN are both learning very similar kinds of representations, but the LSTM achieved higher levels of performance on a quantitative evaluation. We also trained a non-recurrent neural network, Skip-gram, on the same input to compare our results to the state-of-the-art in machine learning. We found that Skip-gram achieves relatively similar performance to the LSTM, but is representing words more in terms of thematic compared to taxonomic relations, and we provide reasons why this might be the case. Our findings show that a learning system that derives abstract, distributed representations for the purpose of predicting sequential dependencies in naturalistic language may provide insight into emergence of many properties of the developing semantic system. PMID:29520243
Remembering episodic memories is not necessary for forgetting of negative words: Semantic retrieval can cause forgetting of negative words.

PubMed

Kobayashi, Masanori; Tanno, Yoshihiko

2015-06-01

Retrieval of a memory can induce forgetting of other related memories, which is known as retrieval-induced forgetting. Although most studies have investigated retrieval-induced forgetting by remembering episodic memories, this also can occur by remembering semantic memories. The present study shows that retrieval of semantic memories can lead to forgetting of negative words. In two experiments, participants learned words and then engaged in retrieval practice where they were asked to recall words related to the learned words from semantic memory. Finally, participants completed a stem-cued recall test for the learned words. The results showed forgetting of neutral and negative words, which was characteristic of semantic retrieval-induced forgetting. A certain degree of overlapping features, except same learning episode, is sufficient to cause retrieval-induced forgetting of negative words. Given the present results, we conclude that retrieval-induced forgetting of negative words does not require recollection of episodic memories.
Navigation as a New Form of Search for Agricultural Learning Resources in Semantic Repositories

NASA Astrophysics Data System (ADS)

Cano, Ramiro; Abián, Alberto; Mena, Elena

Education is essential when it comes to raise public awareness on the environmental and economic benefits of organic agriculture and agroecology (OA & AE). Organic.Edunet, an EU funded project, aims at providing a freely-available portal where learning contents on OA & AE can be published and accessed through specialized technologies. This paper describes a novel mechanism for providing semantic capabilities (such as semantic navigational queries) to an arbitrary set of agricultural learning resources, in the context of the Organic.Edunet initiative.
Do statistical segmentation abilities predict lexical-phonological and lexical-semantic abilities in children with and without SLI?

PubMed Central

Mainela-Arnold, Elina; Evans, Julia L.

2014-01-01

This study tested the predictions of the procedural deficit hypothesis by investigating the relationship between sequential statistical learning and two aspects of lexical ability, lexical-phonological and lexical-semantic, in children with and without specific language impairment (SLI). Participants included 40 children (ages 8;5–12;3), 20 children with SLI and 20 with typical development. Children completed Saffran’s statistical word segmentation task, a lexical-phonological access task (gating task), and a word definition task. Poor statistical learners were also poor at managing lexical-phonological competition during the gating task. However, statistical learning was not a significant predictor of semantic richness in word definitions. The ability to track statistical sequential regularities may be important for learning the inherently sequential structure of lexical-phonology, but not as important for learning lexical-semantic knowledge. Consistent with the procedural/declarative memory distinction, the brain networks associated with the two types of lexical learning are likely to have different learning properties. PMID:23425593
The Role of Self-Teaching in Learning Orthographic and Semantic Aspects of New Words

ERIC Educational Resources Information Center

Ricketts, Jessie; Bishop, Dorothy V. M.; Pimperton, Hannah; Nation, Kate

2011-01-01

This study explores how children learn the meaning (semantics) and spelling patterns (orthography) of novel words encountered in story context. English-speaking children (N = 88) aged 7 to 8 years read 8 stories and each story contained 1 novel word repeated 4 times. Semantic cues were provided by the story context such that children could infer…
Weaknesses in Lexical-Semantic Knowledge among College Students with Specific Learning Disabilities: Evidence from a Semantic Fluency Task

ERIC Educational Resources Information Center

Hall, Jessica; McGregor, Karla K.; Oleson, Jacob

2017-01-01

Purpose: The purpose of this study is to determine whether deficits in executive function and lexical-semantic memory compromise the linguistic performance of young adults with specific learning disabilities (LD) enrolled in postsecondary studies. Method: One hundred eighty-five students with LD (n = 53) or normal language development (ND, n =…
Semantically enabling pharmacogenomic data for the realization of personalized medicine

PubMed Central

Samwald, Matthias; Coulet, Adrien; Huerga, Iker; Powers, Robert L; Luciano, Joanne S; Freimuth, Robert R; Whipple, Frederick; Pichler, Elgar; Prud’hommeaux, Eric; Dumontier, Michel; Marshall, M Scott

2014-01-01

Understanding how each individual’s genetics and physiology influences pharmaceutical response is crucial to the realization of personalized medicine and the discovery and validation of pharmacogenomic biomarkers is key to its success. However, integration of genotype and phenotype knowledge in medical information systems remains a critical challenge. The inability to easily and accurately integrate the results of biomolecular studies with patients’ medical records and clinical reports prevents us from realizing the full potential of pharmacogenomic knowledge for both drug development and clinical practice. Herein, we describe approaches using Semantic Web technologies, in which pharmacogenomic knowledge relevant to drug development and medical decision support is represented in such a way that it can be efficiently accessed both by software and human experts. We suggest that this approach increases the utility of data, and that such computational technologies will become an essential part of personalized medicine, alongside diagnostics and pharmaceutical products. PMID:22256869
The Influence of Concreteness of Concepts on the Integration of Novel Words into the Semantic Network

PubMed Central

Ding, Jinfeng; Liu, Wenjuan; Yang, Yufang

2017-01-01

On the basis of previous studies revealing a processing advantage of concrete words over abstract words, the current study aimed to further explore the influence of concreteness on the integration of novel words into semantic memory with the event related potential (ERP) technique. In the experiment during the learning phase participants read two-sentence contexts and inferred the meaning of novel words. The novel words were two-character non-words in Chinese language. Their meaning was either a concrete or abstract known concept which could be inferred from the contexts. During the testing phase participants performed a lexical decision task in which the learned novel words served as primes for either their corresponding concepts, semantically related or unrelated targets. For the concrete novel words, the semantically related words belonged to the same semantic categories with their corresponding concepts. For the abstract novel words, the semantically related words were synonyms of their corresponding concepts. The unrelated targets were real words which were concrete or abstract for the concrete or abstract novel words respectively. The ERP results showed that the corresponding concepts and the semantically related words elicited smaller N400s than the unrelated words. The N400 effect was not modulated by the concreteness of the concepts. In addition, the concrete corresponding concepts elicited a smaller late positive component (LPC) than the concrete unrelated words. This LPC effect was absent for the abstract words. The results indicate that although both concrete and abstract novel words can be acquired and linked to their related words in the semantic network after a short learning phase, the concrete novel words are learned better. Our findings support the (extended) dual coding theory and broaden our understanding of adult word learning and changes in concept organization. PMID:29255440
The Influence of Concreteness of Concepts on the Integration of Novel Words into the Semantic Network.

PubMed

Ding, Jinfeng; Liu, Wenjuan; Yang, Yufang

2017-01-01

On the basis of previous studies revealing a processing advantage of concrete words over abstract words, the current study aimed to further explore the influence of concreteness on the integration of novel words into semantic memory with the event related potential (ERP) technique. In the experiment during the learning phase participants read two-sentence contexts and inferred the meaning of novel words. The novel words were two-character non-words in Chinese language. Their meaning was either a concrete or abstract known concept which could be inferred from the contexts. During the testing phase participants performed a lexical decision task in which the learned novel words served as primes for either their corresponding concepts, semantically related or unrelated targets. For the concrete novel words, the semantically related words belonged to the same semantic categories with their corresponding concepts. For the abstract novel words, the semantically related words were synonyms of their corresponding concepts. The unrelated targets were real words which were concrete or abstract for the concrete or abstract novel words respectively. The ERP results showed that the corresponding concepts and the semantically related words elicited smaller N400s than the unrelated words. The N400 effect was not modulated by the concreteness of the concepts. In addition, the concrete corresponding concepts elicited a smaller late positive component (LPC) than the concrete unrelated words. This LPC effect was absent for the abstract words. The results indicate that although both concrete and abstract novel words can be acquired and linked to their related words in the semantic network after a short learning phase, the concrete novel words are learned better. Our findings support the (extended) dual coding theory and broaden our understanding of adult word learning and changes in concept organization.
Generating Researcher Networks with Identified Persons on a Semantic Service Platform

NASA Astrophysics Data System (ADS)

Jung, Hanmin; Lee, Mikyoung; Kim, Pyung; Lee, Seungwoo

This paper describes a Semantic Web-based method to acquire researcher networks by means of identification scheme, ontology, and reasoning. Three steps are required to realize it; resolving co-references, finding experts, and generating researcher networks. We adopt OntoFrame as an underlying semantic service platform and apply reasoning to make direct relations between far-off classes in ontology schema. 453,124 Elsevier journal articles with metadata and full-text documents in information technology and biomedical domains have been loaded and served on the platform as a test set.
The Use of Prosody in Semantic and Syntactic Disambiguation: Comparison between Japanese and Chinese Speakers' Sentence Production in English

ERIC Educational Resources Information Center

Tian, Shuang; Murao, Remi

2016-01-01

The present study examined the use of prosody in semantic and syntactic disambiguation by means of comparison between Japanese and Chinese speakers' production of English sentences. In Chinese and Japanese, lexical prosody is more prominent than sentence prosody, and the sentential meaning contrast is usually realized through particles or a change…
The Semantic Web: From Representation to Realization

NASA Astrophysics Data System (ADS)

Thórisson, Kristinn R.; Spivack, Nova; Wissner, James M.

A semantically-linked web of electronic information - the Semantic Web - promises numerous benefits including increased precision in automated information sorting, searching, organizing and summarizing. Realizing this requires significantly more reliable meta-information than is readily available today. It also requires a better way to represent information that supports unified management of diverse data and diverse Manipulation methods: from basic keywords to various types of artificial intelligence, to the highest level of intelligent manipulation - the human mind. How this is best done is far from obvious. Relying solely on hand-crafted annotation and ontologies, or solely on artificial intelligence techniques, seems less likely for success than a combination of the two. In this paper describe an integrated, complete solution to these challenges that has already been implemented and tested with hundreds of thousands of users. It is based on an ontological representational level we call SemCards that combines ontological rigour with flexible user interface constructs. SemCards are machine- and human-readable digital entities that allow non-experts to create and use semantic content, while empowering machines to better assist and participate in the process. SemCards enable users to easily create semantically-grounded data that in turn acts as examples for automation processes, creating a positive iterative feedback loop of metadata creation and refinement between user and machine. They provide a holistic solution to the Semantic Web, supporting powerful management of the full lifecycle of data, including its creation, retrieval, classification, sorting and sharing. We have implemented the SemCard technology on the semantic Web site Twine.com, showing that the technology is indeed versatile and scalable. Here we present the key ideas behind SemCards and describe the initial implementation of the technology.
Games and Simulations in Online Learning: Research and Development Frameworks

ERIC Educational Resources Information Center

Gibson, David; Aldrich, Clark; Prensky, Marc

2007-01-01

Games and Simulations in Online Learning: Research and Development Frameworks examines the potential of games and simulations in online learning, and how the future could look as developers learn to use the emerging capabilities of the Semantic Web. It presents a general understanding of how the Semantic Web will impact education and how games and…
A Case Study on Sepsis Using PubMed and Deep Learning for Ontology Learning.

PubMed

Arguello Casteleiro, Mercedes; Maseda Fernandez, Diego; Demetriou, George; Read, Warren; Fernandez Prieto, Maria Jesus; Des Diz, Julio; Nenadic, Goran; Keane, John; Stevens, Robert

2017-01-01

We investigate the application of distributional semantics models for facilitating unsupervised extraction of biomedical terms from unannotated corpora. Term extraction is used as the first step of an ontology learning process that aims to (semi-)automatic annotation of biomedical concepts and relations from more than 300K PubMed titles and abstracts. We experimented with both traditional distributional semantics methods such as Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA) as well as the neural language models CBOW and Skip-gram from Deep Learning. The evaluation conducted concentrates on sepsis, a major life-threatening condition, and shows that Deep Learning models outperform LSA and LDA with much higher precision.
The role of sleep spindles and slow-wave activity in integrating new information in semantic memory.

PubMed

Tamminen, Jakke; Lambon Ralph, Matthew A; Lewis, Penelope A

2013-09-25

Assimilating new information into existing knowledge is a fundamental part of consolidating new memories and allowing them to guide behavior optimally and is vital for conceptual knowledge (semantic memory), which is accrued over many years. Sleep is important for memory consolidation, but its impact upon assimilation of new information into existing semantic knowledge has received minimal examination. Here, we examined the integration process by training human participants on novel words with meanings that fell into densely or sparsely populated areas of semantic memory in two separate sessions. Overnight sleep was polysomnographically monitored after each training session and recall was tested immediately after training, after a night of sleep, and 1 week later. Results showed that participants learned equal numbers of both word types, thus equating amount and difficulty of learning across the conditions. Measures of word recognition speed showed a disadvantage for novel words in dense semantic neighborhoods, presumably due to interference from many semantically related concepts, suggesting that the novel words had been successfully integrated into semantic memory. Most critically, semantic neighborhood density influenced sleep architecture, with participants exhibiting more sleep spindles and slow-wave activity after learning the sparse compared with the dense neighborhood words. These findings provide the first evidence that spindles and slow-wave activity mediate integration of new information into existing semantic networks.
Nonword Repetition and Vocabulary Knowledge as Predictors of Children's Phonological and Semantic Word Learning.

PubMed

Adlof, Suzanne M; Patten, Hannah

2017-03-01

This study examined the unique and shared variance that nonword repetition and vocabulary knowledge contribute to children's ability to learn new words. Multiple measures of word learning were used to assess recall and recognition of phonological and semantic information. Fifty children, with a mean age of 8 years (range 5-12 years), completed experimental assessments of word learning and norm-referenced assessments of receptive and expressive vocabulary knowledge and nonword repetition skills. Hierarchical multiple regression analyses examined the variance in word learning that was explained by vocabulary knowledge and nonword repetition after controlling for chronological age. Together with chronological age, nonword repetition and vocabulary knowledge explained up to 44% of the variance in children's word learning. Nonword repetition was the stronger predictor of phonological recall, phonological recognition, and semantic recognition, whereas vocabulary knowledge was the stronger predictor of verbal semantic recall. These findings extend the results of past studies indicating that both nonword repetition skill and existing vocabulary knowledge are important for new word learning, but the relative influence of each predictor depends on the way word learning is measured. Suggestions for further research involving typically developing children and children with language or reading impairments are discussed.
Item-specific and generalization effects on brain activation when learning Chinese characters

PubMed Central

Deng, Yuan; Booth, James R.; Chou, Tai-Li; Ding, Guo-Sheng; Peng, Dan-Ling

2009-01-01

Neural changes related to learning of the meaning of Chinese characters in English speakers were examined using functional magnetic resonance imaging (fMRI). We examined item specific learning effects for trained characters, but also the generalization of semantic knowledge to novel transfer characters that shared a semantic radical (part of a character that gives a clue to word meaning, e.g. water for lake) with trained characters. Behavioral results show that acquired semantic knowledge improves performance for both trained and transfer characters. Neuroimaging results show that the left fusiform gyrus plays a central role in the visual processing of orthographic information in characters. The left superior parietal cortex seems to play a crucial role in learning the visual–spatial aspects of the characters because it shows learning related decreases for trained characters, is correlated with behavioral improvement from early to late in learning for the trained characters, and is correlated with better long-term retention for the transfer characters. The inferior frontal gyrus seems to be associated with the efficiency of retrieving and manipulating semantic representations because there are learning related decreases for trained characters and this decrease is correlated with greater behavioral improvement from early to late in learning. PMID:18514678
Do semantic contextual cues facilitate transfer learning from video in toddlers?

PubMed Central

Zimmermann, Laura; Moser, Alecia; Grenell, Amanda; Dickerson, Kelly; Yao, Qianwen; Gerhardstein, Peter; Barr, Rachel

2015-01-01

Young children typically demonstrate a transfer deficit, learning less from video than live presentations. Semantically meaningful context has been demonstrated to enhance learning in young children. We examined the effect of a semantically meaningful context on toddlers’ imitation performance. Two- and 2.5-year-olds participated in a puzzle imitation task to examine learning from either a live or televised model. The model demonstrated how to assemble a three-piece puzzle to make a fish or a boat, with the puzzle demonstration occurring against a semantically meaningful background context (ocean) or a yellow background (no context). Participants in the video condition performed significantly worse than participants in the live condition, demonstrating the typical transfer deficit effect. While the context helped improve overall levels of imitation, especially for the boat puzzle, only individual differences in the ability to self-generate a stimulus label were associated with a reduction in the transfer deficit. PMID:26029131
Do semantic contextual cues facilitate transfer learning from video in toddlers?

PubMed

Zimmermann, Laura; Moser, Alecia; Grenell, Amanda; Dickerson, Kelly; Yao, Qianwen; Gerhardstein, Peter; Barr, Rachel

2015-01-01

Young children typically demonstrate a transfer deficit, learning less from video than live presentations. Semantically meaningful context has been demonstrated to enhance learning in young children. We examined the effect of a semantically meaningful context on toddlers' imitation performance. Two- and 2.5-year-olds participated in a puzzle imitation task to examine learning from either a live or televised model. The model demonstrated how to assemble a three-piece puzzle to make a fish or a boat, with the puzzle demonstration occurring against a semantically meaningful background context (ocean) or a yellow background (no context). Participants in the video condition performed significantly worse than participants in the live condition, demonstrating the typical transfer deficit effect. While the context helped improve overall levels of imitation, especially for the boat puzzle, only individual differences in the ability to self-generate a stimulus label were associated with a reduction in the transfer deficit.
Towards a Semantic E-Learning Theory by Using a Modelling Approach

ERIC Educational Resources Information Center

Yli-Luoma, Pertti V. J.; Naeve, Ambjorn

2006-01-01

In the present study, a semantic perspective on e-learning theory is advanced and a modelling approach is used. This modelling approach towards the new learning theory is based on the four SECI phases of knowledge conversion: Socialisation, Externalisation, Combination and Internalisation, introduced by Nonaka in 1994, and involving two levels of…

Effects of Semantic Ambiguity Detection Training on Reading Comprehension Achievement of English Learners with Learning Difficulties

ERIC Educational Resources Information Center

Jozwik, Sara L.; Douglas, Karen H.

2016-01-01

This study examined how explicit instruction in semantic ambiguity detection affected the reading comprehension and metalinguistic awareness of five English learners (ELs) with learning difficulties (e.g., attention deficit/hyperactivity disorder, specific learning disability). A multiple probe across participants design (Gast & Ledford, 2010)…
Semantics of User Interface for Image Retrieval: Possibility Theory and Learning Techniques.

ERIC Educational Resources Information Center

Crehange, M.; And Others

1989-01-01

Discusses the need for a rich semantics for the user interface in interactive image retrieval and presents two methods for building such interfaces: possibility theory applied to fuzzy data retrieval, and a machine learning technique applied to learning the user's deep need. Prototypes developed using videodisks and knowledge-based software are…
The Influence of Consistency, Frequency, and Semantics on Learning to Read: An Artificial Orthography Paradigm

ERIC Educational Resources Information Center

Taylor, J. S. H.; Plunkett, Kim; Nation, Kate

2011-01-01

Two experiments explored learning, generalization, and the influence of semantics on orthographic processing in an artificial language. In Experiment 1, 16 adults learned to read 36 novel words written in novel characters. Posttraining, participants discriminated trained from untrained items and generalized to novel items, demonstrating extraction…
Item-Specific and Generalization Effects on Brain Activation when Learning Chinese Characters

ERIC Educational Resources Information Center

Deng, Yuan; Booth, James R.; Chou, Tai-Li; Ding, Guo-Sheng; Peng, Dan-Ling

2008-01-01

Neural changes related to learning of the meaning of Chinese characters in English speakers were examined using functional magnetic resonance imaging (fMRI). We examined item specific learning effects for trained characters, but also the generalization of semantic knowledge to novel transfer characters that shared a semantic radical (part of a…
Interconnected growing self-organizing maps for auditory and semantic acquisition modeling.

PubMed

Cao, Mengxue; Li, Aijun; Fang, Qiang; Kaufmann, Emily; Kröger, Bernd J

2014-01-01

Based on the incremental nature of knowledge acquisition, in this study we propose a growing self-organizing neural network approach for modeling the acquisition of auditory and semantic categories. We introduce an Interconnected Growing Self-Organizing Maps (I-GSOM) algorithm, which takes associations between auditory information and semantic information into consideration, in this paper. Direct phonetic-semantic association is simulated in order to model the language acquisition in early phases, such as the babbling and imitation stages, in which no phonological representations exist. Based on the I-GSOM algorithm, we conducted experiments using paired acoustic and semantic training data. We use a cyclical reinforcing and reviewing training procedure to model the teaching and learning process between children and their communication partners. A reinforcing-by-link training procedure and a link-forgetting procedure are introduced to model the acquisition of associative relations between auditory and semantic information. Experimental results indicate that (1) I-GSOM has good ability to learn auditory and semantic categories presented within the training data; (2) clear auditory and semantic boundaries can be found in the network representation; (3) cyclical reinforcing and reviewing training leads to a detailed categorization as well as to a detailed clustering, while keeping the clusters that have already been learned and the network structure that has already been developed stable; and (4) reinforcing-by-link training leads to well-perceived auditory-semantic associations. Our I-GSOM model suggests that it is important to associate auditory information with semantic information during language acquisition. Despite its high level of abstraction, our I-GSOM approach can be interpreted as a biologically-inspired neurocomputational model.
Recommendation of standardized health learning contents using archetypes and semantic web technologies.

PubMed

Legaz-García, María del Carmen; Martínez-Costa, Catalina; Menárguez-Tortosa, Marcos; Fernández-Breis, Jesualdo Tomás

2012-01-01

Linking Electronic Healthcare Records (EHR) content to educational materials has been considered a key international recommendation to enable clinical engagement and to promote patient safety. This would suggest citizens to access reliable information available on the web and to guide them properly. In this paper, we describe an approach in that direction, based on the use of dual model EHR standards and standardized educational contents. The recommendation method will be based on the semantic coverage of the learning content repository for a particular archetype, which will be calculated by applying semantic web technologies like ontologies and semantic annotations.
Levodopa enhances explicit new-word learning in healthy adults: a preliminary study.

PubMed

Shellshear, Leanne; MacDonald, Anna D; Mahoney, Jeffrey; Finch, Emma; McMahon, Katie; Silburn, Peter; Nathan, Pradeep J; Copland, David A

2015-09-01

While the role of dopamine in modulating executive function, working memory and associative learning has been established; its role in word learning and language processing more generally is not clear. This preliminary study investigated the impact of increased synaptic dopamine levels on new-word learning ability in healthy young adults using an explicit learning paradigm. A double-blind, placebo-controlled, between-groups design was used. Participants completed five learning sessions over 1 week with levodopa or placebo administered at each session (five doses, 100 mg). Each session involved a study phase followed by a test phase. Test phases involved recall and recognition tests of the new (non-word) names previously paired with unfamiliar objects (half with semantic descriptions) during the study phase. The levodopa group showed superior recall accuracy for new words over five learning sessions compared with the placebo group and better recognition accuracy at a 1-month follow-up for words learnt with a semantic description. These findings suggest that dopamine boosts initial lexical acquisition and enhances longer-term consolidation of words learnt with semantic information, consistent with dopaminergic enhancement of semantic salience. Copyright © 2015 John Wiley & Sons, Ltd.
Transductive multi-view zero-shot learning.

PubMed

Fu, Yanwei; Hospedales, Timothy M; Xiang, Tao; Gong, Shaogang

2015-11-01

Most existing zero-shot learning approaches exploit transfer learning via an intermediate semantic representation shared between an annotated auxiliary dataset and a target dataset with different classes and no annotation. A projection from a low-level feature space to the semantic representation space is learned from the auxiliary dataset and applied without adaptation to the target dataset. In this paper we identify two inherent limitations with these approaches. First, due to having disjoint and potentially unrelated classes, the projection functions learned from the auxiliary dataset/domain are biased when applied directly to the target dataset/domain. We call this problem the projection domain shift problem and propose a novel framework, transductive multi-view embedding, to solve it. The second limitation is the prototype sparsity problem which refers to the fact that for each target class, only a single prototype is available for zero-shot learning given a semantic representation. To overcome this problem, a novel heterogeneous multi-view hypergraph label propagation method is formulated for zero-shot learning in the transductive embedding space. It effectively exploits the complementary information offered by different semantic representations and takes advantage of the manifold structures of multiple representation spaces in a coherent manner. We demonstrate through extensive experiments that the proposed approach (1) rectifies the projection shift between the auxiliary and target domains, (2) exploits the complementarity of multiple semantic representations, (3) significantly outperforms existing methods for both zero-shot and N-shot recognition on three image and video benchmark datasets, and (4) enables novel cross-view annotation tasks.
Name and face learning in older adults: effects of level of processing, self-generation, and intention to learn.

PubMed

Troyer, Angela K; Häfliger, Andrea; Cadieux, Mélanie J; Craik, Fergus I M

2006-03-01

Many older adults are interested in strategies to help them learn new names. We examined the learning conditions that provide maximal benefit to name and face learning. In Experiment 1, consistent with levels-of-processing theory, name recall and recognition by 20 younger and 20 older adults was poorest with physical processing, intermediate with phonemic processing, and best with semantic processing. In Experiment 2, name and face learning in 20 younger and 20 older adults was maximized with semantic processing of names and physical processing of faces. Experiment 3 showed a benefit of self-generation and of intentional learning of name-face pairs in 24 older adults. Findings suggest that memory interventions should emphasize processing names semantically, processing faces physically, self-generating this information, and keeping in mind that memory for the names will be needed in the future.
Verbal Memory and Semantic Organization of Children with Learning Disabilities

ERIC Educational Resources Information Center

Polychroni, Fotini; Economou, Alexandra; Printezi, Anna; Koutlidi, Ifigeneia

2011-01-01

The present study examined the verbal learning performance and the semantic organization used by Greek reading-disabled readers as compared to a control group using a list-learning task. The sample consisted of 45 elementary school children with reading difficulties and 45 comparison children matched for age and gender. Tests of reading ability,…
A Semantic-Oriented Approach for Organizing and Developing Annotation for E-Learning

ERIC Educational Resources Information Center

Brut, Mihaela M.; Sedes, Florence; Dumitrescu, Stefan D.

2011-01-01

This paper presents a solution to extend the IEEE LOM standard with ontology-based semantic annotations for efficient use of learning objects outside Learning Management Systems. The data model corresponding to this approach is first presented. The proposed indexing technique for this model development in order to acquire a better annotation of…
Case-Based Learning, Pedagogical Innovation, and Semantic Web Technologies

ERIC Educational Resources Information Center

Martinez-Garcia, A.; Morris, S.; Tscholl, M.; Tracy, F.; Carmichael, P.

2012-01-01

This paper explores the potential of Semantic Web technologies to support teaching and learning in a variety of higher education settings in which some form of case-based learning is the pedagogy of choice. It draws on the empirical work of a major three year research and development project in the United Kingdom: "Ensemble: Semantic…
Investigating the Speech Act of Correction in Iraqi EFL Context

ERIC Educational Resources Information Center

Darweesh, Abbas Deygan; Mehdi, Wafaa Sahib

2016-01-01

The present paper investigates the performance of the Iraqi students for the speech act of correction and how it is realized with status unequal. It attempts to achieve the following aims: (1) Setting out the felicity conditions for the speech act of correction in terms of Searle conditions; (2) Identifying the semantic formulas that realize the…
Serial and semantic encoding of lists of words in schizophrenia patients with visual hallucinations.

PubMed

Brébion, Gildas; Ohlsen, Ruth I; Pilowsky, Lyn S; David, Anthony S

2011-03-30

Previous research has suggested that visual hallucinations in schizophrenia are associated with abnormal salience of visual mental images. Since visual imagery is used as a mnemonic strategy to learn lists of words, increased visual imagery might impede the other commonly used strategies of serial and semantic encoding. We had previously published data on the serial and semantic strategies implemented by patients when learning lists of concrete words with different levels of semantic organisation (Brébion et al., 2004). In this paper we present a re-analysis of these data, aiming at investigating the associations between learning strategies and visual hallucinations. Results show that the patients with visual hallucinations presented less serial clustering in the non-organisable list than the other patients. In the semantically organisable list with typical instances, they presented both less serial and less semantic clustering than the other patients. Thus, patients with visual hallucinations demonstrate reduced use of serial and semantic encoding in the lists made up of fairly familiar concrete words, which enable the formation of mental images. Although these results are preliminary, we propose that this different processing of the lists stems from the abnormal salience of the mental images such patients experience from the word stimuli. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.
Nonword Repetition and Vocabulary Knowledge as Predictors of Children's Phonological and Semantic Word Learning

PubMed Central

Patten, Hannah

2017-01-01

Purpose This study examined the unique and shared variance that nonword repetition and vocabulary knowledge contribute to children's ability to learn new words. Multiple measures of word learning were used to assess recall and recognition of phonological and semantic information. Method Fifty children, with a mean age of 8 years (range 5–12 years), completed experimental assessments of word learning and norm-referenced assessments of receptive and expressive vocabulary knowledge and nonword repetition skills. Hierarchical multiple regression analyses examined the variance in word learning that was explained by vocabulary knowledge and nonword repetition after controlling for chronological age. Results Together with chronological age, nonword repetition and vocabulary knowledge explained up to 44% of the variance in children's word learning. Nonword repetition was the stronger predictor of phonological recall, phonological recognition, and semantic recognition, whereas vocabulary knowledge was the stronger predictor of verbal semantic recall. Conclusions These findings extend the results of past studies indicating that both nonword repetition skill and existing vocabulary knowledge are important for new word learning, but the relative influence of each predictor depends on the way word learning is measured. Suggestions for further research involving typically developing children and children with language or reading impairments are discussed. PMID:28241284
Wide coverage biomedical event extraction using multiple partially overlapping corpora

PubMed Central

2013-01-01

Background Biomedical events are key to understanding physiological processes and disease, and wide coverage extraction is required for comprehensive automatic analysis of statements describing biomedical systems in the literature. In turn, the training and evaluation of extraction methods requires manually annotated corpora. However, as manual annotation is time-consuming and expensive, any single event-annotated corpus can only cover a limited number of semantic types. Although combined use of several such corpora could potentially allow an extraction system to achieve broad semantic coverage, there has been little research into learning from multiple corpora with partially overlapping semantic annotation scopes. Results We propose a method for learning from multiple corpora with partial semantic annotation overlap, and implement this method to improve our existing event extraction system, EventMine. An evaluation using seven event annotated corpora, including 65 event types in total, shows that learning from overlapping corpora can produce a single, corpus-independent, wide coverage extraction system that outperforms systems trained on single corpora and exceeds previously reported results on two established event extraction tasks from the BioNLP Shared Task 2011. Conclusions The proposed method allows the training of a wide-coverage, state-of-the-art event extraction system from multiple corpora with partial semantic annotation overlap. The resulting single model makes broad-coverage extraction straightforward in practice by removing the need to either select a subset of compatible corpora or semantic types, or to merge results from several models trained on different individual corpora. Multi-corpus learning also allows annotation efforts to focus on covering additional semantic types, rather than aiming for exhaustive coverage in any single annotation effort, or extending the coverage of semantic types annotated in existing corpora. PMID:23731785
Actively learning human gaze shifting paths for semantics-aware photo cropping.

PubMed

Zhang, Luming; Gao, Yue; Ji, Rongrong; Xia, Yingjie; Dai, Qionghai; Li, Xuelong

2014-05-01

Photo cropping is a widely used tool in printing industry, photography, and cinematography. Conventional cropping models suffer from the following three challenges. First, the deemphasized role of semantic contents that are many times more important than low-level features in photo aesthetics. Second, the absence of a sequential ordering in the existing models. In contrast, humans look at semantically important regions sequentially when viewing a photo. Third, the difficulty of leveraging inputs from multiple users. Experience from multiple users is particularly critical in cropping as photo assessment is quite a subjective task. To address these challenges, this paper proposes semantics-aware photo cropping, which crops a photo by simulating the process of humans sequentially perceiving semantically important regions of a photo. We first project the local features (graphlets in this paper) onto the semantic space, which is constructed based on the category information of the training photos. An efficient learning algorithm is then derived to sequentially select semantically representative graphlets of a photo, and the selecting process can be interpreted by a path, which simulates humans actively perceiving semantics in a photo. Furthermore, we learn a prior distribution of such active graphlet paths from training photos that are marked as aesthetically pleasing by multiple users. The learned priors enforce the corresponding active graphlet path of a test photo to be maximally similar to those from the training photos. Experimental results show that: 1) the active graphlet path accurately predicts human gaze shifting, and thus is more indicative for photo aesthetics than conventional saliency maps and 2) the cropped photos produced by our approach outperform its competitors in both qualitative and quantitative comparisons.
Contribution of organizational strategy to verbal learning and memory in adults with attention-deficit/hyperactivity disorder.

PubMed

Roth, Robert M; Wishart, Heather A; Flashman, Laura A; Riordan, Henry J; Huey, Leighton; Saykin, Andrew J

2004-01-01

Statistical mediation modeling was used to test the hypothesis that poor use of a semantic organizational strategy contributes to verbal learning and memory deficits in adults with attention-deficit/hyperactivity disorder (ADHD). Comparison of 28 adults with ADHD and 34 healthy controls revealed lower performance by the ADHD group on tests of verbal learning and memory, sustained attention, and use of semantic organization during encoding. Mediation modeling indicated that state anxiety, but not semantic organization, significantly contributed to the prediction of both learning and delayed recall in the ADHD group. The pattern of findings suggests that decreased verbal learning and memory in adult ADHD is due in part to situational anxiety and not to poor use of organizational strategies during encoding. ((c) 2004 APA, all rights reserved)
Phonological learning in semantic dementia.

PubMed

Jefferies, Elizabeth; Bott, Samantha; Ehsan, Sheeba; Lambon Ralph, Matthew A

2011-04-01

Patients with semantic dementia (SD) have anterior temporal lobe (ATL) atrophy that gives rise to a highly selective deterioration of semantic knowledge. Despite pronounced anomia and poor comprehension of words and pictures, SD patients have well-formed, fluent speech and normal digit span. Given the intimate connection between phonological STM and word learning revealed by both neuropsychological and developmental studies, SD patients might be expected to show good acquisition of new phonological forms, even though their ability to map these onto meanings is impaired. In contradiction of these predictions, a limited amount of previous research has found poor learning of new phonological forms in SD. In a series of experiments, we examined whether SD patient, GE, could learn novel phonological sequences and, if so, under which circumstances. GE showed normal benefits of phonological knowledge in STM (i.e., normal phonotactic frequency and phonological similarity effects) but reduced support from semantic memory (i.e., poor immediate serial recall for semantically degraded words, characterised by frequent item errors). Next, we demonstrated normal learning of serial order information for repeated lists of single-digit number words using the Hebb paradigm: these items were well-understood allowing them to be repeated without frequent item errors. In contrast, patient GE showed little learning of nonsense syllable sequences using the same Hebb paradigm. Detailed analysis revealed that both GE and the controls showed a tendency to learn their own errors as opposed to the target items. Finally, we showed normal learning of phonological sequences for GE when he was prevented from repeating his errors. These findings confirm that the ATL atrophy in SD disrupts phonological processing for semantically degraded words but leaves the phonological architecture intact. Consequently, when item errors are minimised, phonological STM can support the acquisition of new phoneme sequences in patients with SD. Copyright © 2011 Elsevier Ltd. All rights reserved.
Semantic Categorization: A Comparison between Deaf and Hearing Children

ERIC Educational Resources Information Center

Ormel, Ellen A.; Gijsel, Martine A. R.; Hermans, Daan; Bosman, Anna M. T.; Knoors, Harry; Verhoeven, Ludo

2010-01-01

Learning to read is a major obstacle for children who are deaf. The otherwise significant role of phonology is often limited as a result of hearing loss. However, semantic knowledge may facilitate reading comprehension. One important aspect of semantic knowledge concerns semantic categorization. In the present study, the quality of the semantic…

Semi-Supervised Learning to Identify UMLS Semantic Relations.

PubMed

Luo, Yuan; Uzuner, Ozlem

2014-01-01

The UMLS Semantic Network is constructed by experts and requires periodic expert review to update. We propose and implement a semi-supervised approach for automatically identifying UMLS semantic relations from narrative text in PubMed. Our method analyzes biomedical narrative text to collect semantic entity pairs, and extracts multiple semantic, syntactic and orthographic features for the collected pairs. We experiment with seeded k-means clustering with various distance metrics. We create and annotate a ground truth corpus according to the top two levels of the UMLS semantic relation hierarchy. We evaluate our system on this corpus and characterize the learning curves of different clustering configuration. Using KL divergence consistently performs the best on the held-out test data. With full seeding, we obtain macro-averaged F-measures above 70% for clustering the top level UMLS relations (2-way), and above 50% for clustering the second level relations (7-way).
Interconnected growing self-organizing maps for auditory and semantic acquisition modeling

PubMed Central

Cao, Mengxue; Li, Aijun; Fang, Qiang; Kaufmann, Emily; Kröger, Bernd J.

2014-01-01

Based on the incremental nature of knowledge acquisition, in this study we propose a growing self-organizing neural network approach for modeling the acquisition of auditory and semantic categories. We introduce an Interconnected Growing Self-Organizing Maps (I-GSOM) algorithm, which takes associations between auditory information and semantic information into consideration, in this paper. Direct phonetic–semantic association is simulated in order to model the language acquisition in early phases, such as the babbling and imitation stages, in which no phonological representations exist. Based on the I-GSOM algorithm, we conducted experiments using paired acoustic and semantic training data. We use a cyclical reinforcing and reviewing training procedure to model the teaching and learning process between children and their communication partners. A reinforcing-by-link training procedure and a link-forgetting procedure are introduced to model the acquisition of associative relations between auditory and semantic information. Experimental results indicate that (1) I-GSOM has good ability to learn auditory and semantic categories presented within the training data; (2) clear auditory and semantic boundaries can be found in the network representation; (3) cyclical reinforcing and reviewing training leads to a detailed categorization as well as to a detailed clustering, while keeping the clusters that have already been learned and the network structure that has already been developed stable; and (4) reinforcing-by-link training leads to well-perceived auditory–semantic associations. Our I-GSOM model suggests that it is important to associate auditory information with semantic information during language acquisition. Despite its high level of abstraction, our I-GSOM approach can be interpreted as a biologically-inspired neurocomputational model. PMID:24688478
Nonword Repetition and Vocabulary Knowledge as Predictors of Children's Phonological and Semantic Word Learning

ERIC Educational Resources Information Center

Adlof, Suzanne M.; Patten, Hannah

2017-01-01

Purpose: This study examined the unique and shared variance that nonword repetition and vocabulary knowledge contribute to children's ability to learn new words. Multiple measures of word learning were used to assess recall and recognition of phonological and semantic information. Method: Fifty children, with a mean age of 8 years (range 5-12…
With Some Help from Others' Hands: Iconic Gesture Helps Semantic Learning in Children with Specific Language Impairment

ERIC Educational Resources Information Center

Vogt, Susanne S.; Kauschke, Christina

2017-01-01

Purpose: Semantic learning under 2 co-speech gesture conditions was investigated in children with specific language impairment (SLI) and typically developing (TD) children. Learning was analyzed between conditions. Method: Twenty children with SLI (aged 4 years), 20 TD children matched for age, and 20 TD children matched for language scores were…
A Metadata Model for E-Learning Coordination through Semantic Web Languages

ERIC Educational Resources Information Center

Elci, Atilla

2005-01-01

This paper reports on a study aiming to develop a metadata model for e-learning coordination based on semantic web languages. A survey of e-learning modes are done initially in order to identify content such as phases, activities, data schema, rules and relations, etc. relevant for a coordination model. In this respect, the study looks into the…
Cases, Simulacra, and Semantic Web Technologies

ERIC Educational Resources Information Center

Carmichael, P.; Tscholl, M.

2013-01-01

"Ensemble" is an interdisciplinary research and development project exploring the potential role of emerging Semantic Web technologies in case-based learning across learning environments in higher education. Empirical findings have challenged the claim that cases "bring reality into the classroom" and that this, in turn, might…
Interoperability in Personalized Adaptive Learning

ERIC Educational Resources Information Center

Aroyo, Lora; Dolog, Peter; Houben, Geert-Jan; Kravcik, Milos; Naeve, Ambjorn; Nilsson, Mikael; Wild, Fridolin

2006-01-01

Personalized adaptive learning requires semantic-based and context-aware systems to manage the Web knowledge efficiently as well as to achieve semantic interoperability between heterogeneous information resources and services. The technological and conceptual differences can be bridged either by means of standards or via approaches based on the…
A memory learning framework for effective image retrieval.

PubMed

Han, Junwei; Ngan, King N; Li, Mingjing; Zhang, Hong-Jiang

2005-04-01

Most current content-based image retrieval systems are still incapable of providing users with their desired results. The major difficulty lies in the gap between low-level image features and high-level image semantics. To address the problem, this study reports a framework for effective image retrieval by employing a novel idea of memory learning. It forms a knowledge memory model to store the semantic information by simply accumulating user-provided interactions. A learning strategy is then applied to predict the semantic relationships among images according to the memorized knowledge. Image queries are finally performed based on a seamless combination of low-level features and learned semantics. One important advantage of our framework is its ability to efficiently annotate images and also propagate the keyword annotation from the labeled images to unlabeled images. The presented algorithm has been integrated into a practical image retrieval system. Experiments on a collection of 10,000 general-purpose images demonstrate the effectiveness of the proposed framework.
The dark side of incremental learning: a model of cumulative semantic interference during lexical access in speech production.

PubMed

Oppenheim, Gary M; Dell, Gary S; Schwartz, Myrna F

2010-02-01

Naming a picture of a dog primes the subsequent naming of a picture of a dog (repetition priming) and interferes with the subsequent naming of a picture of a cat (semantic interference). Behavioral studies suggest that these effects derive from persistent changes in the way that words are activated and selected for production, and some have claimed that the findings are only understandable by positing a competitive mechanism for lexical selection. We present a simple model of lexical retrieval in speech production that applies error-driven learning to its lexical activation network. This model naturally produces repetition priming and semantic interference effects. It predicts the major findings from several published experiments, demonstrating that these effects may arise from incremental learning. Furthermore, analysis of the model suggests that competition during lexical selection is not necessary for semantic interference if the learning process is itself competitive. Copyright 2009 Elsevier B.V. All rights reserved.
Acquiring concepts and features of novel words by two types of learning: direct mapping and inference.

PubMed

Chen, Shuang; Wang, Lin; Yang, Yufang

2014-04-01

This study examined the semantic representation of novel words learnt in two conditions: directly mapping a novel word to a concept (Direct mapping: DM) and inferring the concept from provided features (Inferred learning: IF). A condition where no definite concept could be inferred (No basic-level meaning: NM) served as a baseline. The semantic representation of the novel word was assessed via a semantic-relatedness judgment task. In this task, the learned novel word served as a prime, while the corresponding concept, an unlearned feature of the concept, and an unrelated word served as targets. ERP responses to the targets, primed by the novel words in the three learning conditions, were compared. For the corresponding concept, smaller N400s were elicited in the DM and IF conditions than in the NM condition, indicating that the concept could be obtained in both learning conditions. However, for the unlearned feature, the targets in the IF condition produced an N400 effect while in the DM condition elicited an LPC effect relative to the NM learning condition. No ERP difference was observed among the three learning conditions for the unrelated words. The results indicate that conditions of learning affect the semantic representation of novel word, and that the unlearned feature was only activated by the novel word in the IF learning condition. Copyright © 2014 Elsevier Ltd. All rights reserved.
Teaching Semantic Radicals Facilitates Inferring New Character Meaning in Sentence Reading for Nonnative Chinese Speakers

PubMed Central

Nguyen, Thi Phuong; Zhang, Jie; Li, Hong; Wu, Xinchun; Cheng, Yahua

2017-01-01

This study investigates the effects of teaching semantic radicals in inferring the meanings of unfamiliar characters among nonnative Chinese speakers. A total of 54 undergraduates majoring in Chinese Language from a university in Hanoi, Vietnam, who had 1 year of learning experience in Chinese were assigned to two experimental groups that received instructional intervention, called “old-for-new” semantic radical teaching, through two counterbalanced sets of semantic radicals, with one control group. All of the students completed pre- and post-tests of a sentence cloze task where they were required to choose an appropriate character that fit the sentence context among four options. The four options shared the same phonetic radicals but had different semantic radicals. The results showed that the pre-test and post-test score increases were significant for the experimental groups, but not for the control group. Most importantly, the experimental groups successfully transferred the semantic radical strategy to figure out the meanings of unfamiliar characters containing semantic radicals that had not been taught. The results demonstrate the effectiveness of teaching semantic radicals for lexical inference in sentence reading for nonnative speakers, and highlight the ability of transfer learning to acquire semantic categories of sub-lexical units (semantic radicals) in Chinese characters among foreign language learners. PMID:29109694
The Inhibitory Mechanism in Learning Ambiguous Words in a Second Language

PubMed Central

Lu, Yao; Wu, Junjie; Dunlap, Susan; Chen, Baoguo

2017-01-01

Ambiguous words are hard to learn, yet little is known about what causes this difficulty. The current study aimed to investigate the relationship between the representations of new and prior meanings of ambiguous words in second language (L2) learning, and to explore the function of inhibitory control on L2 ambiguous word learning at the initial stage of learning. During a 4-day learning phase, Chinese–English bilinguals learned 30 novel English words for 30 min per day using bilingual flashcards. Half of the words to be learned were unambiguous (had one meaning) and half were ambiguous (had two semantically unrelated meanings learned in sequence). Inhibitory control was introduced as a subject variable measured by a Stroop task. The semantic representations established for the studied items were probed using a cross-language semantic relatedness judgment task, in which the learned English words served as the prime, and the targets were either semantically related or unrelated to the prime. Results showed that response latencies for the second meaning of ambiguous words were slower than for the first meaning and for unambiguous words, and that performance on only the second meaning of ambiguous words was predicted by inhibitory control ability. These results suggest that, at the initial stage of L2 ambiguous word learning, the representation of the second meaning is weak, probably interfered with by the representation of the prior learned meaning. Moreover, inhibitory control may modulate learning of the new meanings, such that individuals with better inhibitory control may more effectively suppress interference from the first meaning, and thus learn the new meaning more quickly. PMID:28496423
The Inhibitory Mechanism in Learning Ambiguous Words in a Second Language.

PubMed

Lu, Yao; Wu, Junjie; Dunlap, Susan; Chen, Baoguo

2017-01-01

Ambiguous words are hard to learn, yet little is known about what causes this difficulty. The current study aimed to investigate the relationship between the representations of new and prior meanings of ambiguous words in second language (L2) learning, and to explore the function of inhibitory control on L2 ambiguous word learning at the initial stage of learning. During a 4-day learning phase, Chinese-English bilinguals learned 30 novel English words for 30 min per day using bilingual flashcards. Half of the words to be learned were unambiguous (had one meaning) and half were ambiguous (had two semantically unrelated meanings learned in sequence). Inhibitory control was introduced as a subject variable measured by a Stroop task. The semantic representations established for the studied items were probed using a cross-language semantic relatedness judgment task, in which the learned English words served as the prime, and the targets were either semantically related or unrelated to the prime. Results showed that response latencies for the second meaning of ambiguous words were slower than for the first meaning and for unambiguous words, and that performance on only the second meaning of ambiguous words was predicted by inhibitory control ability. These results suggest that, at the initial stage of L2 ambiguous word learning, the representation of the second meaning is weak, probably interfered with by the representation of the prior learned meaning. Moreover, inhibitory control may modulate learning of the new meanings, such that individuals with better inhibitory control may more effectively suppress interference from the first meaning, and thus learn the new meaning more quickly.
New Semantic Learning in Patients With Large Medial Temporal Lobe Lesions

PubMed Central

Bayley, P.J.; O'Reilly, R.C.; Curran, T.; Squire, L.R.

2008-01-01

Two patients with large lesions of the medial temporal lobe were given four tests of semantic knowledge that could only have been acquired after the onset of their amnesia. In contrast to previous studies of postmorbid semantic learning, correct answers could be based on a simple, nonspecific sense of familiarity about single words, faces, or objects. According to recent computational models (for example, Norman and O'Reilly (2003) Psychol Rev 110:611–646), this characteristic should be optimal for detecting the kind of semantic learning that might be supported directly by the neocortex. Both patients exhibited some capacity for new learning, albeit at a level substantially below control performances. Notably, the correct answers appeared to reflect declarative memory. It was not the case that the correct answers simply popped out in some automatic way in the absence of any additional knowledge about the items. Rather, the few correct choices made by the patients tended to be accompanied by additional information about the chosen items, and the available knowledge appeared to be similar qualitatively to the kind of factual knowledge that healthy individuals gradually acquire over the years. The results are consistent with the idea that neocortical structures outside the medial temporal lobe are able to support some semantic learning, albeit to a very limited extent. Alternatively, the small amount of learning detected in the present study could depend on tissue within the posterior medial temporal lobe that remains intact in both patients. PMID:18306299
Modeling semantic aspects for cross-media image indexing.

PubMed

Monay, Florent; Gatica-Perez, Daniel

2007-10-01

To go beyond the query-by-example paradigm in image retrieval, there is a need for semantic indexing of large image collections for intuitive text-based image search. Different models have been proposed to learn the dependencies between the visual content of an image set and the associated text captions, then allowing for the automatic creation of semantic indices for unannotated images. The task, however, remains unsolved. In this paper, we present three alternatives to learn a Probabilistic Latent Semantic Analysis model (PLSA) for annotated images, and evaluate their respective performance for automatic image indexing. Under the PLSA assumptions, an image is modeled as a mixture of latent aspects that generates both image features and text captions, and we investigate three ways to learn the mixture of aspects. We also propose a more discriminative image representation than the traditional Blob histogram, concatenating quantized local color information and quantized local texture descriptors. The first learning procedure of a PLSA model for annotated images is a standard EM algorithm, which implicitly assumes that the visual and the textual modalities can be treated equivalently. The other two models are based on an asymmetric PLSA learning, allowing to constrain the definition of the latent space on the visual or on the textual modality. We demonstrate that the textual modality is more appropriate to learn a semantically meaningful latent space, which translates into improved annotation performance. A comparison of our learning algorithms with respect to recent methods on a standard dataset is presented, and a detailed evaluation of the performance shows the validity of our framework.
Semantic Representation of Newly Learned L2 Words and Their Integration in the L2 Lexicon

ERIC Educational Resources Information Center

Bordag, Denisa; Kirschenbaum, Amit; Rogahn, Maria; Opitz, Andreas

2017-01-01

The present semantic priming study explores the integration of newly learnt L2 German words into the L2 semantic network of German advanced learners. It provides additional evidence in support of earlier findings reporting semantic inhibition effects for emergent representations. An inhibitory mechanism is proposed that temporarily decreases the…
Semantic Services in e-Learning: An Argumentation Case Study

ERIC Educational Resources Information Center

Moreale, Emanuela; Vargas-Vera, Maria

2004-01-01

This paper outlines an e-Learning services architecture offering semantic-based services to students and tutors, in particular ways to browse and obtain information through web services. Services could include registration, authentication, tutoring systems, smart question answering for students' queries, automated marking systems and a student…
Influencing Memory Performance in Learning Disabled Students through Semantic Processing.

ERIC Educational Resources Information Center

Walker, Stephen C.; Poteet, James A.

1989-01-01

Thirty learning-disabled and 30 nonhandicapped intermediate grade children were assessed on memory performance for stimulus words, which were presented with congruent and noncongruent rhyming words and semantically congruent and noncongruent sentence frames. Both groups performed significantly better on words encoded using deep level congruent…
A Robust Geometric Model for Argument Classification

NASA Astrophysics Data System (ADS)

Giannone, Cristina; Croce, Danilo; Basili, Roberto; de Cao, Diego

Argument classification is the task of assigning semantic roles to syntactic structures in natural language sentences. Supervised learning techniques for frame semantics have been recently shown to benefit from rich sets of syntactic features. However argument classification is also highly dependent on the semantics of the involved lexicals. Empirical studies have shown that domain dependence of lexical information causes large performance drops in outside domain tests. In this paper a distributional approach is proposed to improve the robustness of the learning model against out-of-domain lexical phenomena.
Semantic SenseLab: implementing the vision of the Semantic Web in neuroscience

PubMed Central

Samwald, Matthias; Chen, Huajun; Ruttenberg, Alan; Lim, Ernest; Marenco, Luis; Miller, Perry; Shepherd, Gordon; Cheung, Kei-Hoi

2011-01-01

Summary Objective Integrative neuroscience research needs a scalable informatics framework that enables semantic integration of diverse types of neuroscience data. This paper describes the use of the Web Ontology Language (OWL) and other Semantic Web technologies for the representation and integration of molecular-level data provided by several of SenseLab suite of neuroscience databases. Methods Based on the original database structure, we semi-automatically translated the databases into OWL ontologies with manual addition of semantic enrichment. The SenseLab ontologies are extensively linked to other biomedical Semantic Web resources, including the Subcellular Anatomy Ontology, Brain Architecture Management System, the Gene Ontology, BIRNLex and UniProt. The SenseLab ontologies have also been mapped to the Basic Formal Ontology and Relation Ontology, which helps ease interoperability with many other existing and future biomedical ontologies for the Semantic Web. In addition, approaches to representing contradictory research statements are described. The SenseLab ontologies are designed for use on the Semantic Web that enables their integration into a growing collection of biomedical information resources. Conclusion We demonstrate that our approach can yield significant potential benefits and that the Semantic Web is rapidly becoming mature enough to realize its anticipated promises. The ontologies are available online at http://neuroweb.med.yale.edu/senselab/ PMID:20006477

Semantic SenseLab: Implementing the vision of the Semantic Web in neuroscience.

PubMed

Samwald, Matthias; Chen, Huajun; Ruttenberg, Alan; Lim, Ernest; Marenco, Luis; Miller, Perry; Shepherd, Gordon; Cheung, Kei-Hoi

2010-01-01

Integrative neuroscience research needs a scalable informatics framework that enables semantic integration of diverse types of neuroscience data. This paper describes the use of the Web Ontology Language (OWL) and other Semantic Web technologies for the representation and integration of molecular-level data provided by several of SenseLab suite of neuroscience databases. Based on the original database structure, we semi-automatically translated the databases into OWL ontologies with manual addition of semantic enrichment. The SenseLab ontologies are extensively linked to other biomedical Semantic Web resources, including the Subcellular Anatomy Ontology, Brain Architecture Management System, the Gene Ontology, BIRNLex and UniProt. The SenseLab ontologies have also been mapped to the Basic Formal Ontology and Relation Ontology, which helps ease interoperability with many other existing and future biomedical ontologies for the Semantic Web. In addition, approaches to representing contradictory research statements are described. The SenseLab ontologies are designed for use on the Semantic Web that enables their integration into a growing collection of biomedical information resources. We demonstrate that our approach can yield significant potential benefits and that the Semantic Web is rapidly becoming mature enough to realize its anticipated promises. The ontologies are available online at http://neuroweb.med.yale.edu/senselab/. 2009 Elsevier B.V. All rights reserved.
Neurocognitive Mechanisms of Learning to Read: Print Tuning in Beginning Readers Related to Word-Reading Fluency and Semantics but Not Phonology

ERIC Educational Resources Information Center

Eberhard-Moscicka, Aleksandra K.; Jost, Lea B.; Raith, Margit; Maurer, Urs

2015-01-01

During reading acquisition children learn to recognize orthographic stimuli and link them to phonology and semantics. The present study investigated neurocognitive processes of learning to read after one year of schooling. We aimed to elucidate the cognitive processes underlying neural tuning for print that has been shown to play an important role…
Effects of Semantic Web Based Learning on Pre-Service Teachers' ICT Learning Achievement and Satisfaction

ERIC Educational Resources Information Center

Karalar, Halit; Korucu, Agah Tugrul

2016-01-01

Although the Semantic Web offers many opportunities for learners, effects of it in the classroom is not well known. Therefore, in this study explanations have been stated as how the learning objects defined by means of using the terminology in a developed ontology and kept in objects repository should be presented to learners with the aim of…
Grounding statistical learning in context: The effects of learning and retrieval contexts on cross-situational word learning.

PubMed

Chen, Chi-Hsin; Yu, Chen

2017-06-01

Natural language environments usually provide structured contexts for learning. This study examined the effects of semantically themed contexts-in both learning and retrieval phases-on statistical word learning. Results from 2 experiments consistently showed that participants had higher performance in semantically themed learning contexts. In contrast, themed retrieval contexts did not affect performance. Our work suggests that word learners are sensitive to statistical regularities not just at the level of individual word-object co-occurrences but also at another level containing a whole network of associations among objects and their properties.
Epsiodic and Semantic Memory Components of Verbal Paired-Associate Learning.

ERIC Educational Resources Information Center

Elwood, Richard W.

1997-01-01

This study examined correlations between hard (low-associate) and easy (high-associate) verbal paired associates and episodic and semantic memory in a mixed clinical sample of 91 male veterans. The study concludes that hard paired-associate learning should not be presumed to measure episodic memory selectively. (SLD)
Integrating Experiential and Distributional Data to Learn Semantic Representations

ERIC Educational Resources Information Center

Andrews, Mark; Vigliocco, Gabriella; Vinson, David

2009-01-01

The authors identify 2 major types of statistical data from which semantic representations can be learned. These are denoted as "experiential data" and "distributional data". Experiential data are derived by way of experience with the physical world and comprise the sensory-motor data obtained through sense receptors. Distributional data, by…
How Visual and Semantic Information Influence Learning in Familiar Contexts

ERIC Educational Resources Information Center

Goujon, Annabelle; Brockmole, James R.; Ehinger, Krista A.

2012-01-01

Previous research using the contextual cuing paradigm has revealed both quantitative and qualitative differences in learning depending on whether repeated contexts are defined by letter arrays or real-world scenes. To clarify the relative contributions of visual features and semantic information likely to account for such differences, the typical…
Does Testing Increase Spontaneous Mediation in Learning Semantically Related Paired Associates?

ERIC Educational Resources Information Center

Cho, Kit W.; Neely, James H.; Brennan, Michael K.; Vitrano, Deana; Crocco, Stephanie

2017-01-01

Carpenter (2011) argued that the testing effect she observed for semantically related but associatively unrelated paired associates supports the mediator effectiveness hypothesis. This hypothesis asserts that after the cue-target pair "mother-child" is learned, relative to restudying mother-child, a review test in which…
Learning for Semantic Parsing Using Statistical Syntactic Parsing Techniques

DTIC Science & Technology

2010-05-01

Workshop on Supervisory Con- trol of Learning and Adaptive Systems. San Jose, CA. Roland Kuhn and Renato De Mori (1995). The application of semantic...Processing (EMNLP-09), pp. 1–10. Suntec,Singapore. Ana-Maria Popescu, Alex Armanasu, Oren Etzioni, David Ko and Alexander Yates (2004). Modern natural
Leveraging the Semantic Web for Adaptive Education

ERIC Educational Resources Information Center

Kravcik, Milos; Gasevic, Dragan

2007-01-01

In the area of technology-enhanced learning reusability and interoperability issues essentially influence the productivity and efficiency of learning and authoring solutions. There are two basic approaches how to overcome these problems--one attempts to do it via standards and the other by means of the Semantic Web. In practice, these approaches…
Semantic Web-Driven LMS Architecture towards a Holistic Learning Process Model Focused on Personalization

ERIC Educational Resources Information Center

Kerkiri, Tania

2010-01-01

A comprehensive presentation is here made on the modular architecture of an e-learning platform with a distinctive emphasis on content personalization, combining advantages from semantic web technology, collaborative filtering and recommendation systems. Modules of this architecture handle information about both the domain-specific didactic…
RuleML-Based Learning Object Interoperability on the Semantic Web

ERIC Educational Resources Information Center

Biletskiy, Yevgen; Boley, Harold; Ranganathan, Girish R.

2008-01-01

Purpose: The present paper aims to describe an approach for building the Semantic Web rules for interoperation between heterogeneous learning objects, namely course outlines from different universities, and one of the rule uses: identifying (in)compatibilities between course descriptions. Design/methodology/approach: As proof of concept, a rule…
A neural network model of semantic memory linking feature-based object representation and words.

PubMed

Cuppini, C; Magosso, E; Ursino, M

2009-06-01

Recent theories in cognitive neuroscience suggest that semantic memory is a distributed process, which involves many cortical areas and is based on a multimodal representation of objects. The aim of this work is to extend a previous model of object representation to realize a semantic memory, in which sensory-motor representations of objects are linked with words. The model assumes that each object is described as a collection of features, coded in different cortical areas via a topological organization. Features in different objects are segmented via gamma-band synchronization of neural oscillators. The feature areas are further connected with a lexical area, devoted to the representation of words. Synapses among the feature areas, and among the lexical area and the feature areas are trained via a time-dependent Hebbian rule, during a period in which individual objects are presented together with the corresponding words. Simulation results demonstrate that, during the retrieval phase, the network can deal with the simultaneous presence of objects (from sensory-motor inputs) and words (from acoustic inputs), can correctly associate objects with words and segment objects even in the presence of incomplete information. Moreover, the network can realize some semantic links among words representing objects with shared features. These results support the idea that semantic memory can be described as an integrated process, whose content is retrieved by the co-activation of different multimodal regions. In perspective, extended versions of this model may be used to test conceptual theories, and to provide a quantitative assessment of existing data (for instance concerning patients with neural deficits).
Categorizing with gender: does implicit grammatical gender affect semantic processing in 24-month-old toddlers?

PubMed

Bobb, Susan C; Mani, Nivedita

2013-06-01

The current study investigated the interaction of implicit grammatical gender and semantic category knowledge during object identification. German-learning toddlers (24-month-olds) were presented with picture pairs and heard a noun (without a preceding article) labeling one of the pictures. Labels for target and distracter images either matched or mismatched in grammatical gender and either matched or mismatched in semantic category. When target and distracter overlapped in both semantic and gender information, target recognition was impaired compared with when target and distracter overlapped on only one dimension. Results suggest that by 24 months of age, German-learning toddlers are already forming not only semantic but also grammatical gender categories and that these sources of information are activated, and interact, during object identification. Copyright © 2013 Elsevier Inc. All rights reserved.
Abstraction and generalization in statistical learning: implications for the relationship between semantic types and episodic tokens

PubMed Central

2017-01-01

Statistical approaches to emergent knowledge have tended to focus on the process by which experience of individual episodes accumulates into generalizable experience across episodes. However, there is a seemingly opposite, but equally critical, process that such experience affords: the process by which, from a space of types (e.g. onions—a semantic class that develops through exposure to individual episodes involving individual onions), we can perceive or create, on-the-fly, a specific token (a specific onion, perhaps one that is chopped) in the absence of any prior perceptual experience with that specific token. This article reviews a selection of statistical learning studies that lead to the speculation that this process—the generation, on the basis of semantic memory, of a novel episodic representation—is itself an instance of a statistical, in fact associative, process. The article concludes that the same processes that enable statistical abstraction across individual episodes to form semantic memories also enable the generation, from those semantic memories, of representations that correspond to individual tokens, and of novel episodic facts about those tokens. Statistical learning is a window onto these deeper processes that underpin cognition. This article is part of the themed issue ‘New frontiers for statistical learning in the cognitive sciences’. PMID:27872378
Abstraction and generalization in statistical learning: implications for the relationship between semantic types and episodic tokens.

PubMed

Altmann, Gerry T M

2017-01-05

Statistical approaches to emergent knowledge have tended to focus on the process by which experience of individual episodes accumulates into generalizable experience across episodes. However, there is a seemingly opposite, but equally critical, process that such experience affords: the process by which, from a space of types (e.g. onions-a semantic class that develops through exposure to individual episodes involving individual onions), we can perceive or create, on-the-fly, a specific token (a specific onion, perhaps one that is chopped) in the absence of any prior perceptual experience with that specific token. This article reviews a selection of statistical learning studies that lead to the speculation that this process-the generation, on the basis of semantic memory, of a novel episodic representation-is itself an instance of a statistical, in fact associative, process. The article concludes that the same processes that enable statistical abstraction across individual episodes to form semantic memories also enable the generation, from those semantic memories, of representations that correspond to individual tokens, and of novel episodic facts about those tokens. Statistical learning is a window onto these deeper processes that underpin cognition.This article is part of the themed issue 'New frontiers for statistical learning in the cognitive sciences'. © 2016 The Author(s).
Novel word learning in older adults: A role for sleep?

PubMed Central

Kurdziel, Laura B. F.; Mantua, Janna; Spencer, Rebecca M. C.

2016-01-01

Sleep is an offline period during which newly acquired semantic information is transformed into longer-lasting memories. Language acquisition, which requires new word learning and semantic integration, is preferentially benefitted by a period of sleep in children and young adults. Specific features of sleep (e.g., sleep stage characteristics) have been associated with enhanced language acquisition and generalization. However, with increasing age, even in healthy individuals, sleep quality and quantity decrease. Simultaneously, deficits in word retrieval and new word learning emerge. It is unknown whether age-related alterations in language ability are linked with alterations in sleep. The goal of this review is to examine changes in language learning and sleep across the lifespan. We consider how sleep detriments that occur with aging could affect abilities to learn novel words and semantic generalization and propose hypotheses to motivate future research in this area. PMID:27291336
Weaknesses in Lexical-Semantic Knowledge Among College Students With Specific Learning Disabilities: Evidence From a Semantic Fluency Task

PubMed Central

McGregor, Karla K.; Oleson, Jacob

2017-01-01

Purpose The purpose of this study is to determine whether deficits in executive function and lexical-semantic memory compromise the linguistic performance of young adults with specific learning disabilities (LD) enrolled in postsecondary studies. Method One hundred eighty-five students with LD (n = 53) or normal language development (ND, n = 132) named items in the categories animals and food for 1 minute for each category and completed tests of lexical-semantic knowledge and executive control of memory. Groups were compared on total names, mean cluster size, frequency of embedded clusters, frequency of cluster switches, and change in fluency over time. Secondary analyses of variability within the LD group were also conducted. Results The LD group was less fluent than the ND group. Within the LD group, lexical-semantic knowledge predicted semantic fluency and cluster size; executive control of memory predicted semantic fluency and cluster switches. The LD group produced smaller clusters and fewer embedded clusters than the ND group. Groups did not differ in switching or change over time. Conclusions Deficits in the lexical-semantic system associated with LD may persist into young adulthood, even among those who have managed their disability well enough to attend college. Lexical-semantic deficits are associated with compromised semantic fluency, and the two problems are more likely among students with more severe disabilities. PMID:28267833
Weaknesses in Lexical-Semantic Knowledge Among College Students With Specific Learning Disabilities: Evidence From a Semantic Fluency Task.

PubMed

Hall, Jessica; McGregor, Karla K; Oleson, Jacob

2017-03-01

The purpose of this study is to determine whether deficits in executive function and lexical-semantic memory compromise the linguistic performance of young adults with specific learning disabilities (LD) enrolled in postsecondary studies. One hundred eighty-five students with LD (n = 53) or normal language development (ND, n = 132) named items in the categories animals and food for 1 minute for each category and completed tests of lexical-semantic knowledge and executive control of memory. Groups were compared on total names, mean cluster size, frequency of embedded clusters, frequency of cluster switches, and change in fluency over time. Secondary analyses of variability within the LD group were also conducted. The LD group was less fluent than the ND group. Within the LD group, lexical-semantic knowledge predicted semantic fluency and cluster size; executive control of memory predicted semantic fluency and cluster switches. The LD group produced smaller clusters and fewer embedded clusters than the ND group. Groups did not differ in switching or change over time. Deficits in the lexical-semantic system associated with LD may persist into young adulthood, even among those who have managed their disability well enough to attend college. Lexical-semantic deficits are associated with compromised semantic fluency, and the two problems are more likely among students with more severe disabilities.
Semantic and phonological coding in poor and normal readers.

PubMed

Vellutino, F R; Scanlon, D M; Spearing, D

1995-02-01

Three studies were conducted evaluating semantic and phonological coding deficits as alternative explanations of reading disability. In the first study, poor and normal readers in second and sixth grade were compared on various tests evaluating semantic development as well as on tests evaluating rapid naming and pseudoword decoding as independent measures of phonological coding ability. In a second study, the same subjects were given verbal memory and visual-verbal learning tasks using high and low meaning words as verbal stimuli and Chinese ideographs as visual stimuli. On the semantic tasks, poor readers performed below the level of the normal readers only at the sixth grade level, but, on the rapid naming and pseudoword learning tasks, they performed below the normal readers at the second as well as at the sixth grade level. On both the verbal memory and visual-verbal learning tasks, performance in poor readers approximated that of normal readers when the word stimuli were high in meaning but not when they were low in meaning. These patterns were essentially replicated in a third study that used some of the same semantic and phonological measures used in the first experiment, and verbal memory and visual-verbal learning tasks that employed word lists and visual stimuli (novel alphabetic characters) that more closely approximated those used in learning to read. It was concluded that semantic coding deficits are an unlikely cause of reading difficulties in most poor readers at the beginning stages of reading skills acquisition, but accrue as a consequence of prolonged reading difficulties in older readers. It was also concluded that phonological coding deficits are a probable cause of reading difficulties in most poor readers.

Episodic and semantic memory in children with mesial temporal sclerosis.

PubMed

Rzezak, Patricia; Guimarães, Catarina; Fuentes, Daniel; Guerreiro, Marilisa M; Valente, Kette Dualibi Ramos

2011-07-01

The aim of this study was to analyze semantic and episodic memory deficits in children with mesial temporal sclerosis (MTS) and their correlation with clinical epilepsy variables. For this purpose, 19 consecutive children and adolescents with MTS (8 to 16 years old) were evaluated and their performance on five episodic memory tests (short- and long-term memory and learning) and four semantic memory tests was compared with that of 28 healthy volunteers. Patients performed worse on tests of immediate and delayed verbal episodic memory, visual episodic memory, verbal and visual learning, mental scanning for semantic clues, object naming, word definition, and repetition of sentences. Clinical variables such as early age at seizure onset, severity of epilepsy, and polytherapy impaired distinct types of memory. These data confirm that children with MTS have episodic memory deficits and add new information on semantic memory. The data also demonstrate that clinical variables contribute differently to episodic and semantic memory performance. Copyright © 2011 Elsevier Inc. All rights reserved.
The neural correlates of semantic richness: evidence from an fMRI study of word learning.

PubMed

Ferreira, Roberto A; Göbel, Silke M; Hymers, Mark; Ellis, Andrew W

2015-04-01

We investigated the neural correlates of concrete nouns with either many or few semantic features. A group of 21 participants underwent two days of training and were then asked to categorize 40 newly learned words and a set of matched familiar words as living or nonliving in an MRI scanner. Our results showed that the most reliable effects of semantic richness were located in the left angular gyrus (AG) and middle temporal gyrus (MTG), where activation was higher for semantically rich than poor words. Other areas showing the same pattern included bilateral precuneus and posterior cingulate gyrus. Our findings support the view that AG and anterior MTG, as part of the multimodal network, play a significant role in representing and integrating semantic features from different input modalities. We propose that activation in bilateral precuneus and posterior cingulate gyrus reflects interplay between AG and episodic memory systems during semantic retrieval. Copyright © 2015 Elsevier Inc. All rights reserved.
Integrating semantic information into multiple kernels for protein-protein interaction extraction from biomedical literatures.

PubMed

Li, Lishuang; Zhang, Panpan; Zheng, Tianfu; Zhang, Hongying; Jiang, Zhenchao; Huang, Degen

2014-01-01

Protein-Protein Interaction (PPI) extraction is an important task in the biomedical information extraction. Presently, many machine learning methods for PPI extraction have achieved promising results. However, the performance is still not satisfactory. One reason is that the semantic resources were basically ignored. In this paper, we propose a multiple-kernel learning-based approach to extract PPIs, combining the feature-based kernel, tree kernel and semantic kernel. Particularly, we extend the shortest path-enclosed tree kernel (SPT) by a dynamic extended strategy to retrieve the richer syntactic information. Our semantic kernel calculates the protein-protein pair similarity and the context similarity based on two semantic resources: WordNet and Medical Subject Heading (MeSH). We evaluate our method with Support Vector Machine (SVM) and achieve an F-score of 69.40% and an AUC of 92.00%, which show that our method outperforms most of the state-of-the-art systems by integrating semantic information.
Productive extension of semantic memory in school-aged children: Relations with reading comprehension and deployment of cognitive resources.

PubMed

Bauer, Patricia J; Blue, Shala N; Xu, Aoxiang; Esposito, Alena G

2016-07-01

We investigated 7- to 10-year-old children's productive extension of semantic memory through self-generation of new factual knowledge derived through integration of separate yet related facts learned through instruction or through reading. In Experiment 1, an experimenter read the to-be-integrated facts. Children successfully learned and integrated the information and used it to further extend their semantic knowledge, as evidenced by high levels of correct responses in open-ended and forced-choice testing. In Experiment 2, on half of the trials, the to-be-integrated facts were read by an experimenter (as in Experiment 1) and on half of the trials, children read the facts themselves. Self-generation performance was high in both conditions (experimenter- and self-read); in both conditions, self-generation of new semantic knowledge was related to an independent measure of children's reading comprehension. In Experiment 3, the way children deployed cognitive resources during reading was predictive of their subsequent recall of newly learned information derived through integration. These findings indicate self-generation of new semantic knowledge through integration in school-age children as well as relations between this productive means of extension of semantic memory and cognitive processes engaged during reading. (PsycINFO Database Record (c) 2016 APA, all rights reserved).
Productive Extension of Semantic Memory in School-aged Children: Relations with Reading Comprehension and Deployment of Cognitive Resources

PubMed Central

Bauer, Patricia J.; Blue, Shala N.; Xu, Aoxiang; Esposito, Alena G.

2016-01-01

We investigated 7- to 10-year-old children’s productive extension of semantic memory through self-generation of new factual knowledge derived through integration of separate yet related facts learned through instruction or through reading. In Experiment 1, an experimenter read the to-be-integrated facts. Children successfully learned and integrated the information and used it to further extend their semantic knowledge, as evidenced by high levels of correct responses in open-ended and forced-choice testing. In Experiment 2, on half of the trials, the to-be-integrated facts were read by an experimenter (as in Experiment 1) and on half of the trials, children read the facts themselves. Self-generation performance was high in both conditions (experimenter- and self-read); in both conditions, self-generation of new semantic knowledge was related to an independent measure of children’s reading comprehension. In Experiment 3, the way children deployed cognitive resources during reading was predictive of their subsequent recall of newly learned information derived through integration. These findings indicate self-generation of new semantic knowledge through integration in school-age children as well as relations between this productive means of extension of semantic memory and cognitive processes engaged during reading. PMID:27253263
ANALYTiC: An Active Learning System for Trajectory Classification.

PubMed

Soares Junior, Amilcar; Renso, Chiara; Matwin, Stan

2017-01-01

The increasing availability and use of positioning devices has resulted in large volumes of trajectory data. However, semantic annotations for such data are typically added by domain experts, which is a time-consuming task. Machine-learning algorithms can help infer semantic annotations from trajectory data by learning from sets of labeled data. Specifically, active learning approaches can minimize the set of trajectories to be annotated while preserving good performance measures. The ANALYTiC web-based interactive tool visually guides users through this annotation process.
Semantics by analogy for illustrative volume visualization☆

PubMed Central

Gerl, Moritz; Rautek, Peter; Isenberg, Tobias; Gröller, Eduard

2012-01-01

We present an interactive graphical approach for the explicit specification of semantics for volume visualization. This explicit and graphical specification of semantics for volumetric features allows us to visually assign meaning to both input and output parameters of the visualization mapping. This is in contrast to the implicit way of specifying semantics using transfer functions. In particular, we demonstrate how to realize a dynamic specification of semantics which allows to flexibly explore a wide range of mappings. Our approach is based on three concepts. First, we use semantic shader augmentation to automatically add rule-based rendering functionality to static visualization mappings in a shader program, while preserving the visual abstraction that the initial shader encodes. With this technique we extend recent developments that define a mapping between data attributes and visual attributes with rules, which are evaluated using fuzzy logic. Second, we let users define the semantics by analogy through brushing on renderings of the data attributes of interest. Third, the rules are specified graphically in an interface that provides visual clues for potential modifications. Together, the presented methods offer a high degree of freedom in the specification and exploration of rule-based mappings and avoid the limitations of a linguistic rule formulation. PMID:23576827
An Interactive Multimedia Learning Environment for VLSI Built with COSMOS

ERIC Educational Resources Information Center

Angelides, Marios C.; Agius, Harry W.

2002-01-01

This paper presents Bigger Bits, an interactive multimedia learning environment that teaches students about VLSI within the context of computer electronics. The system was built with COSMOS (Content Oriented semantic Modelling Overlay Scheme), which is a modelling scheme that we developed for enabling the semantic content of multimedia to be used…
A Study about Placement Support Using Semantic Similarity

ERIC Educational Resources Information Center

Katz, Marco; van Bruggen, Jan; Giesbers, Bas; Waterink, Wim; Eshuis, Jannes; Koper, Rob

2014-01-01

This paper discusses Latent Semantic Analysis (LSA) as a method for the assessment of prior learning. The Accreditation of Prior Learning (APL) is a procedure to offer learners an individualized curriculum based on their prior experiences and knowledge. The placement decisions in this process are based on the analysis of student material by domain…
Learning the Language of Healthcare Enabling Semantic Web Technology in CHCS

DTIC Science & Technology

2013-09-01

tuples”, (subject, predicate, object), to relate data and achieve semantic interoperability . Other similar technologies exist, but their... Semantic Healthcare repository [5]. Ultimately, both of our data approaches were successful. However, our current test system is based on the CPRS demo...to extract system dependencies and workflows; to extract semantically related patient data ; and to browse patient- centric views into the system . We
Semantics and technologies in modern design of interior stairs

NASA Astrophysics Data System (ADS)

Kukhta, M.; Sokolov, A.; Pelevin, E.

2015-10-01

Use of metal in the design of interior stairs presents new features for shaping, and can be implemented using different technologies. The article discusses the features of design and production technologies of forged metal spiral staircase considering the image semantics based on the historical and cultural heritage. To achieve the objective was applied structural- semantic method (to identify the organization of structure and semantic features of the artistic image), engineering methods (to justify the construction of the object), anthropometry method and ergonomics (to provide usability), methods of comparative analysis (to reveale the features of the way the ladder in different periods of culture). According to the research results are as follows. Was revealed the semantics influence on the design of interior staircase that is based on the World Tree image. Also was suggested rational calculation of steps to ensure the required strength. And finally was presented technology, providing the realization of the artistic image. In the practical part of the work is presented version of forged staircase.
Personal semantic memory: insights from neuropsychological research on amnesia.

PubMed

Grilli, Matthew D; Verfaellie, Mieke

2014-08-01

This paper provides insight into the cognitive and neural mechanisms of personal semantic memory, knowledge that is specific and unique to individuals, by reviewing neuropsychological research on stable amnesia secondary to medial temporal lobe damage. The results reveal that personal semantic memory does not depend on a unitary set of cognitive and neural mechanisms. Findings show that autobiographical fact knowledge reflects an experience-near type of personal semantic memory that relies on the medial temporal lobe for retrieval, albeit less so than personal episodic memory. Additional evidence demonstrates that new autobiographical fact learning likely relies on the medial temporal lobe, but the extent to which remains unclear. Other findings show that retrieval of personal traits/roles and new learning of personal traits/roles and thoughts/beliefs are independent of the medial temporal lobe and thus may represent highly conceptual types of personal semantic memory that are stored in the neocortex. Published by Elsevier Ltd.
[Knowing without remembering: the contribution of developmental amnesia].

PubMed

Lebrun-Givois, C; Guillery-Girard, B; Thomas-Anterion, C; Laurent, B

2008-05-01

The organization of episodic and semantic memory is currently debated, and especially the rule of the hippocampus in the functioning of these two systems. Since theories derived from the observation of the famous patient HM, that highlighted the involvement of this structure in these two systems, numerous studies questioned the implication of the hippocampus in learning a new semantic knowledge. Among these studies, we found Vargha-Kadem's cases of developmental amnesia. In spite of their clear hippocampal atrophy and a massive impairment of episodic memory, these children were able to acquire de novo new semantic knowledge. In the present paper, we describe a new case of developmental amnesia characteristic of this syndrome. In conclusion, the whole published data question the implication of the hippocampus in every semantic learning and suggest the existence of a neocortical network, slower and that needs more exposures to semantic stimuli than the hippocampal one, which can supply a massive hippocampal impairment.
Automatic Semantic Facilitation in Anterior Temporal Cortex Revealed through Multimodal Neuroimaging

PubMed Central

Gramfort, Alexandre; Hämäläinen, Matti S.; Kuperberg, Gina R.

2013-01-01

A core property of human semantic processing is the rapid, facilitatory influence of prior input on extracting the meaning of what comes next, even under conditions of minimal awareness. Previous work has shown a number of neurophysiological indices of this facilitation, but the mapping between time course and localization—critical for separating automatic semantic facilitation from other mechanisms—has thus far been unclear. In the current study, we used a multimodal imaging approach to isolate early, bottom-up effects of context on semantic memory, acquiring a combination of electroencephalography (EEG), magnetoencephalography (MEG), and functional magnetic resonance imaging (fMRI) measurements in the same individuals with a masked semantic priming paradigm. Across techniques, the results provide a strikingly convergent picture of early automatic semantic facilitation. Event-related potentials demonstrated early sensitivity to semantic association between 300 and 500 ms; MEG localized the differential neural response within this time window to the left anterior temporal cortex, and fMRI localized the effect more precisely to the left anterior superior temporal gyrus, a region previously implicated in semantic associative processing. However, fMRI diverged from early EEG/MEG measures in revealing semantic enhancement effects within frontal and parietal regions, perhaps reflecting downstream attempts to consciously access the semantic features of the masked prime. Together, these results provide strong evidence that automatic associative semantic facilitation is realized as reduced activity within the left anterior superior temporal cortex between 300 and 500 ms after a word is presented, and emphasize the importance of multimodal neuroimaging approaches in distinguishing the contributions of multiple regions to semantic processing. PMID:24155321
Network-based high level data classification.

PubMed

Silva, Thiago Christiano; Zhao, Liang

2012-06-01

Traditional supervised data classification considers only physical features (e.g., distance or similarity) of the input data. Here, this type of learning is called low level classification. On the other hand, the human (animal) brain performs both low and high orders of learning and it has facility in identifying patterns according to the semantic meaning of the input data. Data classification that considers not only physical attributes but also the pattern formation is, here, referred to as high level classification. In this paper, we propose a hybrid classification technique that combines both types of learning. The low level term can be implemented by any classification technique, while the high level term is realized by the extraction of features of the underlying network constructed from the input data. Thus, the former classifies the test instances by their physical features or class topologies, while the latter measures the compliance of the test instances to the pattern formation of the data. Our study shows that the proposed technique not only can realize classification according to the pattern formation, but also is able to improve the performance of traditional classification techniques. Furthermore, as the class configuration's complexity increases, such as the mixture among different classes, a larger portion of the high level term is required to get correct classification. This feature confirms that the high level classification has a special importance in complex situations of classification. Finally, we show how the proposed technique can be employed in a real-world application, where it is capable of identifying variations and distortions of handwritten digit images. As a result, it supplies an improvement in the overall pattern recognition rate.
Elaborative Retrieval: Do Semantic Mediators Improve Memory?

ERIC Educational Resources Information Center

Lehman, Melissa; Karpicke, Jeffrey D.

2016-01-01

The elaborative retrieval account of retrieval-based learning proposes that retrieval enhances retention because the retrieval process produces the generation of semantic mediators that link cues to target information. We tested 2 assumptions that form the basis of this account: that semantic mediators are more likely to be generated during…
Semantic and Thematic List Learning of Second Language Vocabulary

ERIC Educational Resources Information Center

Gholami, Javad; Khezrlou, Sima

2014-01-01

This article overviews research on second language vocabulary instruction with a specific focus on semantic and thematic vocabulary-clustering types. The theoretical benefits associated with both the semantic and thematic approaches, as well as the potential problems associated with them, are discussed. The conclusion drawn is that reinforcing the…
Knowledge of Natural Kinds in Semantic Dementia and Alzheimer's Disease

ERIC Educational Resources Information Center

Cross, Katy; Smith, Edward E.; Grossman, Murray

2008-01-01

We examined the semantic impairment for natural kinds in patients with probable Alzheimer's disease (AD) and semantic dementia (SD) using an inductive reasoning paradigm. To learn about the relationships between natural kind exemplars and how these are distinguished from manufactured artifacts, subjects judged the strength of arguments such as…
Facilitation and Interference in Identification of Pictures and Words

DTIC Science & Technology

1994-10-05

semantic activation and episodic memory encoding. Journal of Verbal Learning and Verbal Behavior, 22, 88-104. Becker, C. A. (1979). Semantic context...set of items, such as pictures of common objects or known words, which have representations in semantic memory . To test this, we compared the...activation model in particular because nonwords have no memorial representation in semantic memory and thus cannot interfere with ore another. 2. Long-term
A Model for New Linkages for Prior Learning Assessment

ERIC Educational Resources Information Center

Kalz, Marco; van Bruggen, Jan; Giesbers, Bas; Waterink, Wim; Eshuis, Jannes; Koper, Rob

2008-01-01

Purpose: The purpose of this paper is twofold: first the paper aims to sketch the theoretical basis for the use of electronic portfolios for prior learning assessment; second it endeavours to introduce latent semantic analysis (LSA) as a powerful method for the computation of semantic similarity between texts and a basis for a new observation link…

GenieTutor: A Computer Assisted Second-Language Learning System Based on Semantic and Grammar Correctness Evaluations

ERIC Educational Resources Information Center

Kwon, Oh-Woog; Lee, Kiyoung; Kim, Young-Kil; Lee, Yunkeun

2015-01-01

This paper introduces a Dialog-Based Computer-Assisted second-Language Learning (DB-CALL) system using semantic and grammar correctness evaluations and the results of its experiment. While the system dialogues with English learners about a given topic, it automatically evaluates the grammar and content properness of their English utterances, then…
Investigating Orthographic and Semantic Aspects of Word Learning in Poor Comprehenders

ERIC Educational Resources Information Center

Ricketts, Jessie; Bishop, Dorothy V. M.; Nation, Kate

2008-01-01

This study compared orthographic and semantic aspects of word learning in children who differed in reading comprehension skill. Poor comprehenders and controls matched for age (9-10 years), nonverbal ability and decoding skill were trained to pronounce 20 visually presented nonwords, 10 in a consistent way and 10 in an inconsistent way. They then…
Semantic Features, Perceptual Expectations, and Frequency as Factors in the Learning of Polar Spatial Adjective Concepts.

ERIC Educational Resources Information Center

Dunckley, Candida J. Lutes; Radtke, Robert C.

Two semantic theories of word learning, a perceptual complexity hypothesis (H. Clark, 1970) and a quantitative complexity hypothesis (E. Clark, 1972) were tested by teaching 24 preschoolers and 16 college students CVC labels for five polar spatial adjective concepts having single word representations in English, and for three having no direct…
Using Graphic Organizers to Teach Content Area Material to Students with Learning Disabilities

ERIC Educational Resources Information Center

Dexter, Douglas D.

2012-01-01

A pretest-posttest comparison group design was used to investigate the effects of a semantic mapping lesson plus visual display versus a semantic mapping lesson alone on adolescents' with learning disabilities (LD) ability to gain and maintain factual knowledge from expository social studies material. In addition, a posttest only comparison group…
VastMM-Tag: Semantic Indexing and Browsing of Videos for E-Learning

ERIC Educational Resources Information Center

Morris, Mitchell J.

2012-01-01

Quickly accessing the contents of a video is challenging for users, particularly for unstructured video, which contains no intentional shot boundaries, no chapters, and no apparent edited format. We approach this problem in the domain of lecture videos though the use of machine learning, to gather semantic information about the videos; and through…
Does Learning to Count Involve a Semantic Induction?

ERIC Educational Resources Information Center

Davidson, Kathryn; Eng, Kortney; Barner, David

2012-01-01

We tested the hypothesis that, when children learn to correctly count sets, they make a semantic induction about the meanings of their number words. We tested the logical understanding of number words in 84 children that were classified as "cardinal-principle knowers" by the criteria set forth by Wynn (1992). Results show that these children often…
Productive Extension of Semantic Memory in School-Aged Children: Relations with Reading Comprehension and Deployment of Cognitive Resources

ERIC Educational Resources Information Center

Bauer, Patricia J.; Blue, Shala N.; Xu, Aoxiang; Esposito, Alena G.

2016-01-01

We investigated 7- to 10-year-old children's productive extension of semantic memory through self-generation of new factual knowledge derived through integration of separate yet related facts learned through instruction or through reading. In Experiment 1, an experimenter read the to-be-integrated facts. Children successfully learned and…
Semantic contextual cuing and visual attention.

PubMed

Goujon, Annabelle; Didierjean, André; Marmèche, Evelyne

2009-02-01

Since M. M. Chun and Y. Jiang's (1998) original study, a large body of research based on the contextual cuing paradigm has shown that the visuocognitive system is capable of capturing certain regularities in the environment in an implicit way. The present study investigated whether regularities based on the semantic category membership of the context can be learned implicitly and whether that learning depends on attention. The contextual cuing paradigm was used with lexical displays in which the semantic category of the contextual words either did or did not predict the target location. Experiments 1 and 2 revealed that implicit contextual cuing effects can be extended to semantic category regularities. Experiments 3 and 4 indicated an implicit contextual cuing effect when the predictive context appeared in an attended color but not when the predictive context appeared in an ignored color. However, when the previously ignored context suddenly became attended, it immediately facilitated performance. In contrast, when the previously attended context suddenly became ignored, no benefit was observed. Results suggest that the expression of implicit semantic knowledge depends on attention but that latent learning can nevertheless take place outside the attentional field. Copyright 2009 APA, all rights reserved.
Real-time image annotation by manifold-based biased Fisher discriminant analysis

NASA Astrophysics Data System (ADS)

Ji, Rongrong; Yao, Hongxun; Wang, Jicheng; Sun, Xiaoshuai; Liu, Xianming

2008-01-01

Automatic Linguistic Annotation is a promising solution to bridge the semantic gap in content-based image retrieval. However, two crucial issues are not well addressed in state-of-art annotation algorithms: 1. The Small Sample Size (3S) problem in keyword classifier/model learning; 2. Most of annotation algorithms can not extend to real-time online usage due to their low computational efficiencies. This paper presents a novel Manifold-based Biased Fisher Discriminant Analysis (MBFDA) algorithm to address these two issues by transductive semantic learning and keyword filtering. To address the 3S problem, Co-Training based Manifold learning is adopted for keyword model construction. To achieve real-time annotation, a Bias Fisher Discriminant Analysis (BFDA) based semantic feature reduction algorithm is presented for keyword confidence discrimination and semantic feature reduction. Different from all existing annotation methods, MBFDA views image annotation from a novel Eigen semantic feature (which corresponds to keywords) selection aspect. As demonstrated in experiments, our manifold-based biased Fisher discriminant analysis annotation algorithm outperforms classical and state-of-art annotation methods (1.K-NN Expansion; 2.One-to-All SVM; 3.PWC-SVM) in both computational time and annotation accuracy with a large margin.
Adults' acquisition of novel dimension words: creating a semantic congruity effect.

PubMed

Ryalls, B O; Smith, L B

2000-07-01

The semantic congruity effect is exhibited when adults are asked to compare pairs of items from a series, and their response is faster when the direction of the comparison coincides with the location of the stimuli in the series. For example, people are faster at picking the bigger of 2 big items than the littler of 2 big items. In the 4 experiments presented, adults were taught new dimensional adjectives (mal/ler and borg/er). Characteristics of the learning situation, such as the nature of the stimulus series and the relative frequency of labeling, were varied. Results revealed that the participants who learned the relative meaning of the artificial dimensional adjectives also formed categories and developed a semantic congruity effect regardless of the characteristics of training. These findings have important implications for our understanding of adult acquisition of novel relational words, the relationship between learning such words and categorization, and the explanations of the semantic congruity effect.
Task demand influences relationships among sex, clustering strategy, and recall: 16-word versus 9-word list learning tests.

PubMed

Sunderaraman, Preeti; Blumen, Helena M; DeMatteo, David; Apa, Zoltan L; Cosentino, Stephanie

2013-06-01

We compared the relationships among sex, clustering strategy, and recall across different task demands using the 16-word California Verbal Learning Test-Second Edition (CVLT-II) and the 9-word Philadelphia (repeatable) Verbal Learning Test (PrVLT). Women generally score higher than men on verbal memory tasks, possibly because women tend to use semantic clustering. This sex difference has been established via word-list learning tests such as the CVLT-II. In a retrospective between-group study, we compared how 2 separate groups of cognitively healthy older adults performed on a longer and a shorter verbal learning test. The group completing the CVLT-II had 36 women and 26 men; the group completing the PrVLT had 27 women and 21 men. Overall, multiple regression analyses revealed that semantic clustering was significantly associated with total recall on both tests' lists (P<0.001). Sex differences in recall and semantic clustering diminished with the shorter PrVLT word list. Semantic clustering uniquely influenced recall on both the longer and shorter word lists. However, serial clustering and sex influenced recall depending on the length of the word list (ie, the task demand). These findings suggest a complex nonlinear relationship among verbal memory, clustering strategies, and task demand.
Task Demand Influences Relationships Among Sex, Clustering Strategy, and Recall: 16-Word Versus 9-Word List Learning Tests

PubMed Central

Sunderaraman, Preeti; Blumen, Helena M.; DeMatteo, David; Apa, Zoltan; Cosentino, Stephanie

2013-01-01

Objective We compared the relationships among sex, clustering strategy, and recall across different task demands using the 16-word California Verbal Learning Test–Second Edition (CVLT-II) and the 9-word Philadelphia (repeatable) Verbal Learning Test (PrVLT). Background Women generally score higher than men on verbal memory tasks, possibly because women tend to use semantic clustering. This sex difference has been established via word-list learning tests such as the CVLT-II. Methods In a retrospective between-group study, we compared how 2 separate groups of cognitively healthy older adults performed on a longer and a shorter verbal learning test. The group completing the CVLT-II had 36 women and 26 men; the group completing the PrVLT had 27 women and 21 men. Results Overall, multiple regression analyses revealed that semantic clustering was significantly associated with total recall on both tests’ lists (P < 0.001). Sex differences in recall and semantic clustering diminished with the shorter PrVLT word list. Conclusions Semantic clustering uniquely influenced recall on both the longer and shorter word lists. However, serial clustering and sex influenced recall depending on the length of the word list (ie, the task demand). These findings suggest a complex nonlinear relationship among verbal memory, clustering strategies, and task demand. PMID:23812171
Musical and verbal semantic memory: two distinct neural networks?

PubMed

Groussard, M; Viader, F; Hubert, V; Landeau, B; Abbas, A; Desgranges, B; Eustache, F; Platel, H

2010-02-01

Semantic memory has been investigated in numerous neuroimaging and clinical studies, most of which have used verbal or visual, but only very seldom, musical material. Clinical studies have suggested that there is a relative neural independence between verbal and musical semantic memory. In the present study, "musical semantic memory" is defined as memory for "well-known" melodies without any knowledge of the spatial or temporal circumstances of learning, while "verbal semantic memory" corresponds to general knowledge about concepts, again without any knowledge of the spatial or temporal circumstances of learning. Our aim was to compare the neural substrates of musical and verbal semantic memory by administering the same type of task in each modality. We used high-resolution PET H(2)O(15) to observe 11 young subjects performing two main tasks: (1) a musical semantic memory task, where the subjects heard the first part of familiar melodies and had to decide whether the second part they heard matched the first, and (2) a verbal semantic memory task with the same design, but where the material consisted of well-known expressions or proverbs. The musical semantic memory condition activated the superior temporal area and inferior and middle frontal areas in the left hemisphere and the inferior frontal area in the right hemisphere. The verbal semantic memory condition activated the middle temporal region in the left hemisphere and the cerebellum in the right hemisphere. We found that the verbal and musical semantic processes activated a common network extending throughout the left temporal neocortex. In addition, there was a material-dependent topographical preference within this network, with predominantly anterior activation during musical tasks and predominantly posterior activation during semantic verbal tasks. Copyright (c) 2009 Elsevier Inc. All rights reserved.
Learning the Semantics of Structured Data Sources

ERIC Educational Resources Information Center

Taheriyan, Mohsen

2015-01-01

Information sources such as relational databases, spreadsheets, XML, JSON, and Web APIs contain a tremendous amount of structured data, however, they rarely provide a semantic model to describe their contents. Semantic models of data sources capture the intended meaning of data sources by mapping them to the concepts and relationships defined by a…
High-Dimensional Semantic Space Accounts of Priming

ERIC Educational Resources Information Center

Jones, Michael N.; Kintsch, Walter; Mewhort, Douglas J. K.

2006-01-01

A broad range of priming data has been used to explore the structure of semantic memory and to test between models of word representation. In this paper, we examine the computational mechanisms required to learn distributed semantic representations for words directly from unsupervised experience with language. To best account for the variety of…
Kernel Methods for Mining Instance Data in Ontologies

NASA Astrophysics Data System (ADS)

Bloehdorn, Stephan; Sure, York

The amount of ontologies and meta data available on the Web is constantly growing. The successful application of machine learning techniques for learning of ontologies from textual data, i.e. mining for the Semantic Web, contributes to this trend. However, no principal approaches exist so far for mining from the Semantic Web. We investigate how machine learning algorithms can be made amenable for directly taking advantage of the rich knowledge expressed in ontologies and associated instance data. Kernel methods have been successfully employed in various learning tasks and provide a clean framework for interfacing between non-vectorial data and machine learning algorithms. In this spirit, we express the problem of mining instances in ontologies as the problem of defining valid corresponding kernels. We present a principled framework for designing such kernels by means of decomposing the kernel computation into specialized kernels for selected characteristics of an ontology which can be flexibly assembled and tuned. Initial experiments on real world Semantic Web data enjoy promising results and show the usefulness of our approach.
Can social semantic web techniques foster collaborative curriculum mapping in medicine?

PubMed

Spreckelsen, Cord; Finsterer, Sonja; Cremer, Jan; Schenkat, Hennig

2013-08-15

Curriculum mapping, which is aimed at the systematic realignment of the planned, taught, and learned curriculum, is considered a challenging and ongoing effort in medical education. Second-generation curriculum managing systems foster knowledge management processes including curriculum mapping in order to give comprehensive support to learners, teachers, and administrators. The large quantity of custom-built software in this field indicates a shortcoming of available IT tools and standards. The project reported here aims at the systematic adoption of techniques and standards of the Social Semantic Web to implement collaborative curriculum mapping for a complete medical model curriculum. A semantic MediaWiki (SMW)-based Web application has been introduced as a platform for the elicitation and revision process of the Aachen Catalogue of Learning Objectives (ACLO). The semantic wiki uses a domain model of the curricular context and offers structured (form-based) data entry, multiple views, structured querying, semantic indexing, and commenting for learning objectives ("LOs"). Semantic indexing of learning objectives relies on both a controlled vocabulary of international medical classifications (ICD, MeSH) and a folksonomy maintained by the users. An additional module supporting the global checking of consistency complements the semantic wiki. Statements of the Object Constraint Language define the consistency criteria. We evaluated the application by a scenario-based formative usability study, where the participants solved tasks in the (fictional) context of 7 typical situations and answered a questionnaire containing Likert-scaled items and free-text questions. At present, ACLO contains roughly 5350 operational (ie, specific and measurable) objectives acquired during the last 25 months. The wiki-based user interface uses 13 online forms for data entry and 4 online forms for flexible searches of LOs, and all the forms are accessible by standard Web browsers. The formative usability study yielded positive results (median rating of 2 ("good") in all 7 general usability items) and produced valuable qualitative feedback, especially concerning navigation and comprehensibility. Although not asked to, the participants (n=5) detected critical aspects of the curriculum (similar learning objectives addressed repeatedly and missing objectives), thus proving the system's ability to support curriculum revision. The SMW-based approach enabled an agile implementation of computer-supported knowledge management. The approach, based on standard Social Semantic Web formats and technology, represents a feasible and effectively applicable compromise between answering to the individual requirements of curriculum management at a particular medical school and using proprietary systems.
Event-Related EEG Oscillations to Semantically Unrelated Words in Normal and Learning Disabled Children

ERIC Educational Resources Information Center

Fernandez, Thalia; Harmony, Thalia; Mendoza, Omar; Lopez-Alanis, Paula; Marroquin, Jose Luis; Otero, Gloria; Ricardo-Garcell, Josefina

2012-01-01

Learning disabilities (LD) are one of the most frequent problems for elementary school-aged children. In this paper, event-related EEG oscillations to semantically related and unrelated pairs of words were studied in a group of 18 children with LD not otherwise specified (LD-NOS) and in 16 children with normal academic achievement. We propose that…
The Career Success of an Adult with a Learning Disability: A Psychosocial Study of Amnesic-Semantic Aphasia.

ERIC Educational Resources Information Center

Kershner, John; And Others

1995-01-01

This case study describes a 39-year-old intellectually gifted man with learning disabilities who demonstrated symptoms of amnesic-semantic aphasia at age 13, leading to placement in a class for students with mental retardation and to dropping out of school. The man's remarkable behavioral and cognitive adjustments led to a fulfilling life and…
Phonological and Semantic Cues to Learning from Word-Types

PubMed Central

Richtsmeier, Peter

2017-01-01

Word-types represent the primary form of data for many models of phonological learning, and they often predict performance in psycholinguistic tasks. Word-types are often tacitly defined as phonologically unique words. Yet, an explicit test of this definition is lacking, and natural language patterning suggests that word meaning could also act as a cue to word-type status. This possibility was tested in a statistical phonotactic learning experiment in which phonological and semantic properties of word-types varied. During familiarization, the learning targets—word-medial consonant sequences—were instantiated either by four related word-types or by just one word-type (the experimental frequency factor). The expectation was that more word-types would lead participants to generalize the target sequences. Regarding semantic cues, related word-types were either associated with different referents or all with a single referent. Regarding phonological cues, related word-types differed from each other by one, two, or more phonemes. At test, participants rated novel wordforms for their similarity to the familiarization words. When participants heard four related word-types, they gave higher ratings to test words with the same consonant sequences, irrespective of the phonological and semantic manipulations. The results support the existing phonological definition of word-types. PMID:29187914

The influence of contextual diversity on word learning.

PubMed

Johns, Brendan T; Dye, Melody; Jones, Michael N

2016-08-01

In a series of analyses over mega datasets, Jones, Johns, and Recchia (Canadian Journal of Experimental Psychology, 66(2), 115-124, 2012) and Johns et al. (Journal of the Acoustical Society of America, 132:2, EL74-EL80, 2012) found that a measure of contextual diversity that takes into account the semantic variability of a word's contexts provided a better fit to both visual and spoken word recognition data than traditional measures, such as word frequency or raw context counts. This measure was empirically validated with an artificial language experiment (Jones et al.). The present study extends the empirical results with a unique natural language learning paradigm, which allows for an examination of the semantic representations that are acquired as semantic diversity is varied. Subjects were incidentally exposed to novel words as they rated short selections from articles, books, and newspapers. When novel words were encountered across distinct discourse contexts, subjects were both faster and more accurate at recognizing them than when they were seen in redundant contexts. However, learning across redundant contexts promoted the development of more stable semantic representations. These findings are predicted by a distributional learning model trained on the same materials as our subjects.
The MMI Semantic Framework: Rosetta Stones for Earth Sciences

NASA Astrophysics Data System (ADS)

Rueda, C.; Bermudez, L. E.; Graybeal, J.; Alexander, P.

2009-12-01

Semantic interoperability—the exchange of meaning among computer systems—is needed to successfully share data in Ocean Science and across all Earth sciences. The best approach toward semantic interoperability requires a designed framework, and operationally tested tools and infrastructure within that framework. Currently available technologies make a scientific semantic framework feasible, but its development requires sustainable architectural vision and development processes. This presentation outlines the MMI Semantic Framework, including recent progress on it and its client applications. The MMI Semantic Framework consists of tools, infrastructure, and operational and community procedures and best practices, to meet short-term and long-term semantic interoperability goals. The design and prioritization of the semantic framework capabilities are based on real-world scenarios in Earth observation systems. We describe some key uses cases, as well as the associated requirements for building the overall infrastructure, which is realized through the MMI Ontology Registry and Repository. This system includes support for community creation and sharing of semantic content, ontology registration, version management, and seamless integration of user-friendly tools and application programming interfaces. The presentation describes the architectural components for semantic mediation, registry and repository for vocabularies, ontology, and term mappings. We show how the technologies and approaches in the framework can address community needs for managing and exchanging semantic information. We will demonstrate how different types of users and client applications exploit the tools and services for data aggregation, visualization, archiving, and integration. Specific examples from OOSTethys (http://www.oostethys.org) and the Ocean Observatories Initiative Cyberinfrastructure (http://www.oceanobservatories.org) will be cited. Finally, we show how semantic augmentation of web services standards could be performed using framework tools.
Semantics of data and service registration to advance interdisciplinary information and data access.

NASA Astrophysics Data System (ADS)

Fox, P. P.; McGuinness, D. L.; Raskin, R.; Sinha, A. K.

2008-12-01

In developing an application of semantic web methods and technologies to address the integration of heterogeneous and interdisciplinary earth-science datasets, we have developed methodologies for creating rich semantic descriptions (ontologies) of the application domains. We have leveraged and extended where possible existing ontology frameworks such as SWEET. As a result of this semantic approach, we have also utilized ontologic descriptions of key enabling elements of the application, such as the registration of datasets with ontologies at several levels of granularity. This has enabled the location and usage of the data across disciplines. We are also realizing the need to develop similar semantic registration of web service data holdings as well as those provided with community and/or standard markup languages (e.g. GeoSciML). This level of semantic enablement extending beyond domain terms and relations significantly enhances our ability to provide a coherent semantic data framework for data and information systems. Much of this work is on the frontier of technology development and we will present the current and near-future capabilities we are developing. This work arises from the Semantically-Enabled Science Data Integration (SESDI) project, which is an NASA/ESTO/ACCESS-funded project involving the High Altitude Observatory at the National Center for Atmospheric Research (NCAR), McGuinness Associates Consulting, NASA/JPL and Virginia Polytechnic University.
Supervised Outlier Detection in Large-Scale Mvs Point Clouds for 3d City Modeling Applications

NASA Astrophysics Data System (ADS)

Stucker, C.; Richard, A.; Wegner, J. D.; Schindler, K.

2018-05-01

We propose to use a discriminative classifier for outlier detection in large-scale point clouds of cities generated via multi-view stereo (MVS) from densely acquired images. What makes outlier removal hard are varying distributions of inliers and outliers across a scene. Heuristic outlier removal using a specific feature that encodes point distribution often delivers unsatisfying results. Although most outliers can be identified correctly (high recall), many inliers are erroneously removed (low precision), too. This aggravates object 3D reconstruction due to missing data. We thus propose to discriminatively learn class-specific distributions directly from the data to achieve high precision. We apply a standard Random Forest classifier that infers a binary label (inlier or outlier) for each 3D point in the raw, unfiltered point cloud and test two approaches for training. In the first, non-semantic approach, features are extracted without considering the semantic interpretation of the 3D points. The trained model approximates the average distribution of inliers and outliers across all semantic classes. Second, semantic interpretation is incorporated into the learning process, i.e. we train separate inlieroutlier classifiers per semantic class (building facades, roof, ground, vegetation, fields, and water). Performance of learned filtering is evaluated on several large SfM point clouds of cities. We find that results confirm our underlying assumption that discriminatively learning inlier-outlier distributions does improve precision over global heuristics by up to ≍ 12 percent points. Moreover, semantically informed filtering that models class-specific distributions further improves precision by up to ≍ 10 percent points, being able to remove very isolated building, roof, and water points while preserving inliers on building facades and vegetation.
Priming in Episodic and Semantic Memory.

ERIC Educational Resources Information Center

McKoon, Gail; Ratcliff, Roger

1979-01-01

Four experiments examined priming between newly learned paired associates through two procedures, lexical decision and item recognition. Results argue against a functional separation of the semantic and episodic memory systems. (Author/AM)
Remote semantic memory is impoverished in hippocampal amnesia

PubMed Central

Klooster, Nathaniel B.; Duff, Melissa C.

2015-01-01

The necessity of the hippocampus for acquiring new semantic concepts is a topic of considerable debate. However, it is generally accepted that any role the hippocampus plays in semantic memory is time limited and that previously acquired information becomes independent of the hippocampus over time. This view, along with intact naming and word-definition matching performance in amnesia, has led to the notion that remote semantic memory is intact in patients with hippocampal amnesia. Motivated by perspectives of word learning as a protracted process where additional features and senses of a word are added over time, and by recent discoveries about the time course of hippocampal contributions to on-line relational processing, reconsolidation, and the flexible integration of information, we revisit the notion that remote semantic memory is intact in amnesia. Using measures of semantic richness and vocabulary depth from psycholinguistics and first and second language-learning studies, we examined how much information is associated with previously acquired, highly familiar words in a group of patients with bilateral hippocampal damage and amnesia. Relative to healthy demographically matched comparison participants and a group of brain-damaged comparison participants, the patients with hippocampal amnesia performed significantly worse on both productive and receptive measures of vocabulary depth and semantic richness. These findings suggest that remote semantic memory is impoverished in patients with hippocampal amnesia and that the hippocampus may play a role in the maintenance and updating of semantic memory beyond its initial acquisition. PMID:26474741
Remote semantic memory is impoverished in hippocampal amnesia.

PubMed

Klooster, Nathaniel B; Duff, Melissa C

2015-12-01

The necessity of the hippocampus for acquiring new semantic concepts is a topic of considerable debate. However, it is generally accepted that any role the hippocampus plays in semantic memory is time limited and that previously acquired information becomes independent of the hippocampus over time. This view, along with intact naming and word-definition matching performance in amnesia, has led to the notion that remote semantic memory is intact in patients with hippocampal amnesia. Motivated by perspectives of word learning as a protracted process where additional features and senses of a word are added over time, and by recent discoveries about the time course of hippocampal contributions to on-line relational processing, reconsolidation, and the flexible integration of information, we revisit the notion that remote semantic memory is intact in amnesia. Using measures of semantic richness and vocabulary depth from psycholinguistics and first and second language-learning studies, we examined how much information is associated with previously acquired, highly familiar words in a group of patients with bilateral hippocampal damage and amnesia. Relative to healthy demographically matched comparison participants and a group of brain-damaged comparison participants, the patients with hippocampal amnesia performed significantly worse on both productive and receptive measures of vocabulary depth and semantic richness. These findings suggest that remote semantic memory is impoverished in patients with hippocampal amnesia and that the hippocampus may play a role in the maintenance and updating of semantic memory beyond its initial acquisition. Copyright © 2015 Elsevier Ltd. All rights reserved.
Does the Sound of a Barking Dog Activate its Corresponding Visual Form? An fMRI Investigation of Modality-Specific Semantic Access

PubMed Central

Reilly, Jamie; Garcia, Amanda; Binney, Richard J.

2016-01-01

Much remains to be learned about the neural architecture underlying word meaning. Fully distributed models of semantic memory predict that the sound of a barking dog will conjointly engage a network of distributed sensorimotor spokes. An alternative framework holds that modality-specific features additionally converge within transmodal hubs. Participants underwent functional MRI while covertly naming familiar objects versus newly learned novel objects from only one of their constituent semantic features (visual form, characteristic sound, or point-light motion representation). Relative to the novel object baseline, familiar concepts elicited greater activation within association regions specific to that presentation modality. Furthermore, visual form elicited activation within high-level auditory association cortex. Conversely, environmental sounds elicited activation in regions proximal to visual association cortex. Both conditions commonly engaged a putative hub region within lateral anterior temporal cortex. These results support hybrid semantic models in which local hubs and distributed spokes are dually engaged in service of semantic memory. PMID:27289210
Teaching Vocabulary to Turkish Young Learners in Semantically Related and Semantically Unrelated Sets by Using Digital Storytelling

ERIC Educational Resources Information Center

Aitkuzhinova-Arslan, Ainur; Gün, Süleyman; Üstünel, Eda

2016-01-01

Teaching vocabulary is a comprehensive process in foreign language learning requiring specific techniques of appropriate instruction and accurate strategy. The present study was conducted to examine the effects of teaching vocabulary to Turkish young learners in a semantic clustering way through digital storytelling. To investigate this aim, six…
Tracking lexical consolidation with ERPs: Lexical and semantic-priming effects on N400 and LPC responses to newly-learned words.

PubMed

Bakker, Iske; Takashima, Atsuko; van Hell, Janet G; Janzen, Gabriele; McQueen, James M

2015-12-01

Novel words can be recalled immediately and after little exposure, but require a post-learning consolidation period to show word-like behaviour such as lexical competition. This pattern is thought to reflect a qualitative shift from episodic to lexical representations. However, several studies have reported immediate effects of meaningful novel words on semantic processing, suggesting that integration of novel word meanings may not require consolidation. The current study synthesises and extends these findings by showing a dissociation between lexical and semantic effects on the electrophysiological (N400, LPC) response to novel words. The difference in N400 amplitude between novel and existing words (a lexical effect) decreased significantly after a 24-h consolidation period, providing novel support for the hypothesis that offline consolidation aids lexicalisation. In contrast, novel words preceded by semantically related primes elicited a more positive LPC response (a semantic-priming effect) both before and after consolidation, indicating that certain semantic effects can be observed even when words have not been fully lexicalised. We propose that novel meanings immediately start to contribute to semantic processing, but that the underlying neural processes may shift from strategic to more automatic with consolidation. Copyright © 2015 Elsevier Ltd. All rights reserved.
Wordform Similarity Increases With Semantic Similarity: An Analysis of 100 Languages.

PubMed

Dautriche, Isabelle; Mahowald, Kyle; Gibson, Edward; Piantadosi, Steven T

2017-11-01

Although the mapping between form and meaning is often regarded as arbitrary, there are in fact well-known constraints on words which are the result of functional pressures associated with language use and its acquisition. In particular, languages have been shown to encode meaning distinctions in their sound properties, which may be important for language learning. Here, we investigate the relationship between semantic distance and phonological distance in the large-scale structure of the lexicon. We show evidence in 100 languages from a diverse array of language families that more semantically similar word pairs are also more phonologically similar. This suggests that there is an important statistical trend for lexicons to have semantically similar words be phonologically similar as well, possibly for functional reasons associated with language learning. Copyright © 2016 Cognitive Science Society, Inc.
Contribution of prior semantic knowledge to new episodic learning in amnesia.

PubMed

Kan, Irene P; Alexander, Michael P; Verfaellie, Mieke

2009-05-01

We evaluated whether prior semantic knowledge would enhance episodic learning in amnesia. Subjects studied prices that are either congruent or incongruent with prior price knowledge for grocery and household items and then performed a forced-choice recognition test for the studied prices. Consistent with a previous report, healthy controls' performance was enhanced by price knowledge congruency; however, only a subset of amnesic patients experienced the same benefit. Whereas patients with relatively intact semantic systems, as measured by an anatomical measure (i.e., lesion involvement of anterior and lateral temporal lobes), experienced a significant congruency benefit, patients with compromised semantic systems did not experience a congruency benefit. Our findings suggest that when prior knowledge structures are intact, they can support acquisition of new episodic information by providing frameworks into which such information can be incorporated.
Minimizing the semantic gap in biomedical content-based image retrieval

NASA Astrophysics Data System (ADS)

Guan, Haiying; Antani, Sameer; Long, L. Rodney; Thoma, George R.

2010-03-01

A major challenge in biomedical Content-Based Image Retrieval (CBIR) is to achieve meaningful mappings that minimize the semantic gap between the high-level biomedical semantic concepts and the low-level visual features in images. This paper presents a comprehensive learning-based scheme toward meeting this challenge and improving retrieval quality. The article presents two algorithms: a learning-based feature selection and fusion algorithm and the Ranking Support Vector Machine (Ranking SVM) algorithm. The feature selection algorithm aims to select 'good' features and fuse them using different similarity measurements to provide a better representation of the high-level concepts with the low-level image features. Ranking SVM is applied to learn the retrieval rank function and associate the selected low-level features with query concepts, given the ground-truth ranking of the training samples. The proposed scheme addresses four major issues in CBIR to improve the retrieval accuracy: image feature extraction, selection and fusion, similarity measurements, the association of the low-level features with high-level concepts, and the generation of the rank function to support high-level semantic image retrieval. It models the relationship between semantic concepts and image features, and enables retrieval at the semantic level. We apply it to the problem of vertebra shape retrieval from a digitized spine x-ray image set collected by the second National Health and Nutrition Examination Survey (NHANES II). The experimental results show an improvement of up to 41.92% in the mean average precision (MAP) over conventional image similarity computation methods.
SemanticSCo: A platform to support the semantic composition of services for gene expression analysis.

PubMed

Guardia, Gabriela D A; Ferreira Pires, Luís; da Silva, Eduardo G; de Farias, Cléver R G

2017-02-01

Gene expression studies often require the combined use of a number of analysis tools. However, manual integration of analysis tools can be cumbersome and error prone. To support a higher level of automation in the integration process, efforts have been made in the biomedical domain towards the development of semantic web services and supporting composition environments. Yet, most environments consider only the execution of simple service behaviours and requires users to focus on technical details of the composition process. We propose a novel approach to the semantic composition of gene expression analysis services that addresses the shortcomings of the existing solutions. Our approach includes an architecture designed to support the service composition process for gene expression analysis, and a flexible strategy for the (semi) automatic composition of semantic web services. Finally, we implement a supporting platform called SemanticSCo to realize the proposed composition approach and demonstrate its functionality by successfully reproducing a microarray study documented in the literature. The SemanticSCo platform provides support for the composition of RESTful web services semantically annotated using SAWSDL. Our platform also supports the definition of constraints/conditions regarding the order in which service operations should be invoked, thus enabling the definition of complex service behaviours. Our proposed solution for semantic web service composition takes into account the requirements of different stakeholders and addresses all phases of the service composition process. It also provides support for the definition of analysis workflows at a high-level of abstraction, thus enabling users to focus on biological research issues rather than on the technical details of the composition process. The SemanticSCo source code is available at https://github.com/usplssb/SemanticSCo. Copyright Â© 2017 Elsevier Inc. All rights reserved.
Lexical and semantic representations in the acquisition of L2 cognate and non-cognate words: evidence from two learning methods in children.

PubMed

Comesaña, Montserrat; Soares, Ana Paula; Sánchez-Casas, Rosa; Lima, Cátia

2012-08-01

How bilinguals represent words in two languages and which mechanisms are responsible for second language acquisition are important questions in the bilingual and vocabulary acquisition literature. This study aims to analyse the effect of two learning methods (picture- vs. word-based method) and two types of words (cognates and non-cognates) in early stages of children's L2 acquisition. Forty-eight native speakers of European Portuguese, all sixth graders (mean age = 10.87 years; SD= 0.85), participated in the study. None of them had prior knowledge of Basque (the L2 in this study). After a learning phase in which L2 words were learned either by a picture- or a word-based method, children were tested in a backward-word translation recognition task at two times (immediately vs. one week later). Results showed that the participants made more errors when rejecting semantically related than semantically unrelated words as correct translations (semantic interference effect). The magnitude of this effect was higher in the delayed test condition regardless of the learning method. Moreover, the overall performance of participants from the word-based method was better than the performance of participants from the picture-word method. Results were discussed concerning the most significant bilingual lexical processing models. ©2011 The British Psychological Society.
SEMANTIC3D.NET: a New Large-Scale Point Cloud Classification Benchmark

NASA Astrophysics Data System (ADS)

Hackel, T.; Savinov, N.; Ladicky, L.; Wegner, J. D.; Schindler, K.; Pollefeys, M.

2017-05-01

This paper presents a new 3D point cloud classification benchmark data set with over four billion manually labelled points, meant as input for data-hungry (deep) learning methods. We also discuss first submissions to the benchmark that use deep convolutional neural networks (CNNs) as a work horse, which already show remarkable performance improvements over state-of-the-art. CNNs have become the de-facto standard for many tasks in computer vision and machine learning like semantic segmentation or object detection in images, but have no yet led to a true breakthrough for 3D point cloud labelling tasks due to lack of training data. With the massive data set presented in this paper, we aim at closing this data gap to help unleash the full potential of deep learning methods for 3D labelling tasks. Our semantic3D.net data set consists of dense point clouds acquired with static terrestrial laser scanners. It contains 8 semantic classes and covers a wide range of urban outdoor scenes: churches, streets, railroad tracks, squares, villages, soccer fields and castles. We describe our labelling interface and show that our data set provides more dense and complete point clouds with much higher overall number of labelled points compared to those already available to the research community. We further provide baseline method descriptions and comparison between methods submitted to our online system. We hope semantic3D.net will pave the way for deep learning methods in 3D point cloud labelling to learn richer, more general 3D representations, and first submissions after only a few months indicate that this might indeed be the case.
A Bayesian generative model for learning semantic hierarchies

PubMed Central

Mittelman, Roni; Sun, Min; Kuipers, Benjamin; Savarese, Silvio

2014-01-01

Building fine-grained visual recognition systems that are capable of recognizing tens of thousands of categories, has received much attention in recent years. The well known semantic hierarchical structure of categories and concepts, has been shown to provide a key prior which allows for optimal predictions. The hierarchical organization of various domains and concepts has been subject to extensive research, and led to the development of the WordNet domains hierarchy (Fellbaum, 1998), which was also used to organize the images in the ImageNet (Deng et al., 2009) dataset, in which the category count approaches the human capacity. Still, for the human visual system, the form of the hierarchy must be discovered with minimal use of supervision or innate knowledge. In this work, we propose a new Bayesian generative model for learning such domain hierarchies, based on semantic input. Our model is motivated by the super-subordinate organization of domain labels and concepts that characterizes WordNet, and accounts for several important challenges: maintaining context information when progressing deeper into the hierarchy, learning a coherent semantic concept for each node, and modeling uncertainty in the perception process. PMID:24904452
Young children make their gestural communication systems more language-like: segmentation and linearization of semantic elements in motion events.

PubMed

Clay, Zanna; Pople, Sally; Hood, Bruce; Kita, Sotaro

2014-08-01

Research on Nicaraguan Sign Language, created by deaf children, has suggested that young children use gestures to segment the semantic elements of events and linearize them in ways similar to those used in signed and spoken languages. However, it is unclear whether this is due to children's learning processes or to a more general effect of iterative learning. We investigated whether typically developing children, without iterative learning, segment and linearize information. Gestures produced in the absence of speech to express a motion event were examined in 4-year-olds, 12-year-olds, and adults (all native English speakers). We compared the proportions of gestural expressions that segmented semantic elements into linear sequences and that encoded them simultaneously. Compared with adolescents and adults, children reshaped the holistic stimuli by segmenting and recombining their semantic features into linearized sequences. A control task on recognition memory ruled out the possibility that this was due to different event perception or memory. Young children spontaneously bring fundamental properties of language into their communication system. © The Author(s) 2014.
Cross-language parafoveal semantic processing: Evidence from Korean-Chinese bilinguals.

PubMed

Wang, Aiping; Yeon, Junmo; Zhou, Wei; Shu, Hua; Yan, Ming

2016-02-01

In the present study, we aimed at testing cross-language cognate and semantic preview effects. We tested how native Korean readers who learned Chinese as a second language make use of the parafoveal information during the reading of Chinese sentences. There were 3 types of Korean preview words: cognate translations of the Chinese target words, semantically related noncognate words, and unrelated words. Together with a highly significant cognate preview effect, more critically, we also observed reliable facilitation in processing of the target word from the semantically related previews in all fixation measures. Results from the present study provide first evidence for semantic processing from parafoveally presented Korean words and for cross-language parafoveal semantic processing.
The Fluctuating Development of Cross-Linguistic Semantic Awareness: A Longitudinal Multiple-Case Study

ERIC Educational Resources Information Center

Zheng, Yongyan

2014-01-01

Second language (L2) learners' awareness of first language-second language (L1-L2) semantic differences plays a critical role in L2 vocabulary learning. This study investigates the long-term development of eight university-level Chinese English as a foreign language learners' cross-linguistic semantic awareness over the course of 10 months. A…

Human hippocampus associates information in memory

PubMed Central

Henke, Katharina; Weber, Bruno; Kneifel, Stefan; Wieser, Heinz Gregor; Buck, Alfred

1999-01-01

The hippocampal formation, one of the most complex and vulnerable brain structures, is recognized as a crucial brain area subserving human long-term memory. Yet, its specific functions in memory are controversial. Recent experimental results suggest that the hippocampal contribution to human memory is limited to episodic memory, novelty detection, semantic (deep) processing of information, and spatial memory. We measured the regional cerebral blood flow by positron-emission tomography while healthy volunteers learned pairs of words with different learning strategies. These led to different forms of learning, allowing us to test the degree to which they challenge hippocampal function. Neither novelty detection nor depth of processing activated the hippocampal formation as much as semantically associating the primarily unrelated words in memory. This is compelling evidence for another function of the human hippocampal formation in memory: establishing semantic associations. PMID:10318979
On the Accessibility of Phonological, Orthographic, and Semantic Aspects of Second Language Vocabulary Learning and Their Relationship with Spatial and Linguistic Intelligences

ERIC Educational Resources Information Center

Zarei, Abbas Ali; Aleali, Maryam

2015-01-01

The present study was an attempt to investigate the differences in the accessibility of phonological, semantic, and orthographic aspects of words in L2 vocabulary learning. For this purpose, a sample of 119 Iranian intermediate level EFL students in a private language institute in Karaj was selected. All of the participants received the same…
Perceptually Guided Photo Retargeting.

PubMed

Xia, Yingjie; Zhang, Luming; Hong, Richang; Nie, Liqiang; Yan, Yan; Shao, Ling

2016-04-22

We propose perceptually guided photo retargeting, which shrinks a photo by simulating a human's process of sequentially perceiving visually/semantically important regions in a photo. In particular, we first project the local features (graphlets in this paper) onto a semantic space, wherein visual cues such as global spatial layout and rough geometric context are exploited. Thereafter, a sparsity-constrained learning algorithm is derived to select semantically representative graphlets of a photo, and the selecting process can be interpreted by a path which simulates how a human actively perceives semantics in a photo. Furthermore, we learn the prior distribution of such active graphlet paths (AGPs) from training photos that are marked as esthetically pleasing by multiple users. The learned priors enforce the corresponding AGP of a retargeted photo to be maximally similar to those from the training photos. On top of the retargeting model, we further design an online learning scheme to incrementally update the model with new photos that are esthetically pleasing. The online update module makes the algorithm less dependent on the number and contents of the initial training data. Experimental results show that: 1) the proposed AGP is over 90% consistent with human gaze shifting path, as verified by the eye-tracking data, and 2) the retargeting algorithm outperforms its competitors significantly, as AGP is more indicative of photo esthetics than conventional saliency maps.
Treatment for Anomia in Semantic Dementia

PubMed Central

Henry, Maya L.; Beeson, Pélagie M.; Rapcsak, Steven Z.

2009-01-01

Anomia is a striking and consistent clinical feature of semantic dementia (SD), a progressive aphasia syndrome associated with focal cortical atrophy of the anterior temporal lobes. Word retrieval deficits in patients with SD have been attributed to the loss of conceptual knowledge, resulting in an impairment referred to as semantic anomia. Whereas an abundance of research has been dedicated to treatment for anomia in individuals with focal brain damage due to stroke, considerably less work has been done regarding treatment for patients with progressive language decline. The purpose of this article is to review the available literature concerning the nature and treatment of anomia in individuals with SD. Several studies have shown that new lexical learning remains possible in these patients. However, newly learned information is likely to be constrained by the learning context, and increased reliance on perceptual and autobiographical contextual information may be necessary to provide critical support for new vocabulary acquisition. There is also evidence suggesting that treatment may slow the progression of anomia over time, even affording some protective benefit to lexical items that are not yet lost. However, treatment efforts are likely to be most beneficial at early stages of the disease, when residual semantic knowledge as. well as relatively spared episodic memory may support new learning. PMID:18348092
Fully convolutional networks with double-label for esophageal cancer image segmentation by self-transfer learning

NASA Astrophysics Data System (ADS)

Xue, Di-Xiu; Zhang, Rong; Zhao, Yuan-Yuan; Xu, Jian-Ming; Wang, Ya-Lei

2017-07-01

Cancer recognition is the prerequisite to determine appropriate treatment. This paper focuses on the semantic segmentation task of microvascular morphological types on narrowband images to aid clinical examination of esophageal cancer. The most challenge for semantic segmentation is incomplete-labeling. Our key insight is to build fully convolutional networks (FCNs) with double-label to make pixel-wise predictions. The roi-label indicating ROIs (region of interest) is introduced as extra constraint to guild feature learning. Trained end-to-end, the FCN model with two target jointly optimizes both segmentation of sem-label (semantic label) and segmentation of roi-label within the framework of self-transfer learning based on multi-task learning theory. The learning representation ability of shared convolutional networks for sem-label is improved with support of roi-label via achieving a better understanding of information outside the ROIs. Our best FCN model gives satisfactory segmentation result with mean IU up to 77.8% (pixel accuracy > 90%). The results show that the proposed approach is able to assist clinical diagnosis to a certain extent.
Can Social Semantic Web Techniques Foster Collaborative Curriculum Mapping In Medicine?

PubMed Central

Finsterer, Sonja; Cremer, Jan; Schenkat, Hennig

2013-01-01

Background Curriculum mapping, which is aimed at the systematic realignment of the planned, taught, and learned curriculum, is considered a challenging and ongoing effort in medical education. Second-generation curriculum managing systems foster knowledge management processes including curriculum mapping in order to give comprehensive support to learners, teachers, and administrators. The large quantity of custom-built software in this field indicates a shortcoming of available IT tools and standards. Objective The project reported here aims at the systematic adoption of techniques and standards of the Social Semantic Web to implement collaborative curriculum mapping for a complete medical model curriculum. Methods A semantic MediaWiki (SMW)-based Web application has been introduced as a platform for the elicitation and revision process of the Aachen Catalogue of Learning Objectives (ACLO). The semantic wiki uses a domain model of the curricular context and offers structured (form-based) data entry, multiple views, structured querying, semantic indexing, and commenting for learning objectives (“LOs”). Semantic indexing of learning objectives relies on both a controlled vocabulary of international medical classifications (ICD, MeSH) and a folksonomy maintained by the users. An additional module supporting the global checking of consistency complements the semantic wiki. Statements of the Object Constraint Language define the consistency criteria. We evaluated the application by a scenario-based formative usability study, where the participants solved tasks in the (fictional) context of 7 typical situations and answered a questionnaire containing Likert-scaled items and free-text questions. Results At present, ACLO contains roughly 5350 operational (ie, specific and measurable) objectives acquired during the last 25 months. The wiki-based user interface uses 13 online forms for data entry and 4 online forms for flexible searches of LOs, and all the forms are accessible by standard Web browsers. The formative usability study yielded positive results (median rating of 2 (“good”) in all 7 general usability items) and produced valuable qualitative feedback, especially concerning navigation and comprehensibility. Although not asked to, the participants (n=5) detected critical aspects of the curriculum (similar learning objectives addressed repeatedly and missing objectives), thus proving the system’s ability to support curriculum revision. Conclusions The SMW-based approach enabled an agile implementation of computer-supported knowledge management. The approach, based on standard Social Semantic Web formats and technology, represents a feasible and effectively applicable compromise between answering to the individual requirements of curriculum management at a particular medical school and using proprietary systems. PMID:23948519
Electrophysiology reveals semantic priming at a short SOA irrespective of depth of prime processing.

PubMed

Küper, Kristina; Heil, Martin

2009-04-03

The otherwise robust behavioral semantic priming effect is reduced to the point of being absent when a letter search has to be performed on the prime word. As a result the automaticity of semantic activation has been called into question. It is unclear, however, in how far automatic processes are even measurable in the letter search priming paradigm as the prime task necessitates a long prime-probe stimulus-onset asynchrony (SOA). In a modified procedure, a short SOA can be realized by delaying the prime task response until after participants have made a lexical decision on the probe. While the absence of lexical decision priming has already been demonstrated in this design it seems premature to draw any definite conclusions from this purely behavioral result since event related potential (ERP) measures have been shown to be a more sensitive index of semantic activation. Using the modified paradigm we thus recorded ERP in addition to lexical decision times. Stimuli were presented at two different SOAs (240 ms vs. 840 ms) and participants performed either a grammatical discrimination (Experiment 1) or a letter search (Experiment 2) on the prime. Irrespective of prime task, the modulation of the N400, the ERP correlate of semantic activation, provided clear-cut evidence of semantic processing at the short SOA. Implications for theories of semantic activation as well as the constraints of the delayed prime task procedure are discussed.
How to Constrain and Maintain a Lexicon for the Treatment of Progressive Semantic Naming Deficits: Principles of Item Selection for Formal Semantic Therapy

PubMed Central

Reilly, Jamie

2015-01-01

The progressive degradation of semantic memory is a common feature of many forms of dementia, including Alzheimer’s Disease and the semantic variant of Primary Progressive Aphasia (svPPA). One of the most functionally debilitating effects of this semantic impairment is the inability to name common people and objects (i.e., anomia). Clinical management of a progressive, semantically-based anomia presents extraordinary challenge for neurorehabilitation. Techniques such as errorless learning and spaced-retrieval training show promise for retraining forgotten words. However, we lack complementary detail about what to train (i.e., item selection) and how to flexibly adapt the training to a declining cognitive system. In this position paper, I weigh the relative merits of several treatment rationales (e.g., restore vs. compensate) and advocate for maintenance of known words over reacquisition of forgotten knowledge in the context of semantic treatment paradigms. I propose a system for generating an item pool and outline a set of core principles for training and sustaining a micro-lexicon consisting of approximately 100 words. These principles are informed by lessons learned over the course of a Phase I treatment study targeting language maintenance over a 5-year span in Alzheimer’s Disease and Frontotemporal Degeneration. Finally, I propose a semantic training approach that capitalizes on lexical frequency and repeated training on conceptual structure to offset the loss of key vocabulary as disease severity worsens. PMID:25609229
A boosting framework for visuality-preserving distance metric learning and its application to medical image retrieval.

PubMed

Yang, Liu; Jin, Rong; Mummert, Lily; Sukthankar, Rahul; Goode, Adam; Zheng, Bin; Hoi, Steven C H; Satyanarayanan, Mahadev

2010-01-01

Similarity measurement is a critical component in content-based image retrieval systems, and learning a good distance metric can significantly improve retrieval performance. However, despite extensive study, there are several major shortcomings with the existing approaches for distance metric learning that can significantly affect their application to medical image retrieval. In particular, "similarity" can mean very different things in image retrieval: resemblance in visual appearance (e.g., two images that look like one another) or similarity in semantic annotation (e.g., two images of tumors that look quite different yet are both malignant). Current approaches for distance metric learning typically address only one goal without consideration of the other. This is problematic for medical image retrieval where the goal is to assist doctors in decision making. In these applications, given a query image, the goal is to retrieve similar images from a reference library whose semantic annotations could provide the medical professional with greater insight into the possible interpretations of the query image. If the system were to retrieve images that did not look like the query, then users would be less likely to trust the system; on the other hand, retrieving images that appear superficially similar to the query but are semantically unrelated is undesirable because that could lead users toward an incorrect diagnosis. Hence, learning a distance metric that preserves both visual resemblance and semantic similarity is important. We emphasize that, although our study is focused on medical image retrieval, the problem addressed in this work is critical to many image retrieval systems. We present a boosting framework for distance metric learning that aims to preserve both visual and semantic similarities. The boosting framework first learns a binary representation using side information, in the form of labeled pairs, and then computes the distance as a weighted Hamming distance using the learned binary representation. A boosting algorithm is presented to efficiently learn the distance function. We evaluate the proposed algorithm on a mammographic image reference library with an Interactive Search-Assisted Decision Support (ISADS) system and on the medical image data set from ImageCLEF. Our results show that the boosting framework compares favorably to state-of-the-art approaches for distance metric learning in retrieval accuracy, with much lower computational cost. Additional evaluation with the COREL collection shows that our algorithm works well for regular image data sets.
Handedness and language learning disability differentially distribute in progressive aphasia variants.

PubMed

Miller, Zachary A; Mandelli, Maria Luisa; Rankin, Katherine P; Henry, Maya L; Babiak, Miranda C; Frazier, Darvis T; Lobach, Iryna V; Bettcher, Brianne M; Wu, Teresa Q; Rabinovici, Gil D; Graff-Radford, Neill R; Miller, Bruce L; Gorno-Tempini, Maria Luisa

2013-11-01

Primary progressive aphasia is a neurodegenerative clinical syndrome that presents in adulthood with an isolated, progressive language disorder. Three main clinical/anatomical variants have been described, each associated with distinctive pathology. A high frequency of neurodevelopmental learning disability in primary progressive aphasia has been reported. Because the disorder is heterogeneous with different patterns of cognitive, anatomical and biological involvement, we sought to identify whether learning disability had a predilection for one or more of the primary progressive aphasia subtypes. We screened the University of California San Francisco Memory and Aging Center's primary progressive aphasia cohort (n = 198) for history of language-related learning disability as well as hand preference, which has associations with learning disability. The study included logopenic (n = 48), non-fluent (n = 54) and semantic (n = 96) variant primary progressive aphasias. We investigated whether the presence of learning disability or non-right-handedness was associated with differential effects on demographic, neuropsychological and neuroimaging features of primary progressive aphasia. We showed that a high frequency of learning disability was present only in the logopenic group (χ(2) = 15.17, P < 0.001) and (χ(2) = 11.51, P < 0.001) compared with semantic and non-fluent populations. In this group, learning disability was associated with earlier onset of disease, more isolated language symptoms, and more focal pattern of left posterior temporoparietal atrophy. Non-right-handedness was instead over-represented in the semantic group, at nearly twice the prevalence of the general population (χ(2) = 6.34, P = 0.01). Within semantic variant primary progressive aphasia the right-handed and non-right-handed cohorts appeared homogeneous on imaging, cognitive profile, and structural analysis of brain symmetry. Lastly, the non-fluent group showed no increase in learning disability or non-right-handedness. Logopenic variant primary progressive aphasia and developmental dyslexia both manifest with phonological disturbances and posterior temporal involvement. Learning disability might confer vulnerability of this network to early-onset, focal Alzheimer's pathology. Left-handedness has been described as a proxy for atypical brain hemispheric lateralization. As non-right-handedness was increased only in the semantic group, anomalous lateralization mechanisms might instead be related to frontotemporal lobar degeneration with abnormal TARDBP. Taken together, this study suggests that neurodevelopmental signatures impart differential trajectories towards neurodegenerative disease.
SemanticOrganizer: A Customizable Semantic Repository for Distributed NASA Project Teams

NASA Technical Reports Server (NTRS)

Keller, Richard M.; Berrios, Daniel C.; Carvalho, Robert E.; Hall, David R.; Rich, Stephen J.; Sturken, Ian B.; Swanson, Keith J.; Wolfe, Shawn R.

2004-01-01

SemanticOrganizer is a collaborative knowledge management system designed to support distributed NASA projects, including diverse teams of scientists, engineers, and accident investigators. The system provides a customizable, semantically structured information repository that stores work products relevant to multiple projects of differing types. SemanticOrganizer is one of the earliest and largest semantic web applications deployed at NASA to date, and has been used in diverse contexts ranging from the investigation of Space Shuttle Columbia's accident to the search for life on other planets. Although the underlying repository employs a single unified ontology, access control and ontology customization mechanisms make the repository contents appear different for each project team. This paper describes SemanticOrganizer, its customization facilities, and a sampling of its applications. The paper also summarizes some key lessons learned from building and fielding a successful semantic web application across a wide-ranging set of domains with diverse users.
Research on Extension of Sparql Ontology Query Language Considering the Computation of Indoor Spatial Relations

NASA Astrophysics Data System (ADS)

Li, C.; Zhu, X.; Guo, W.; Liu, Y.; Huang, H.

2015-05-01

A method suitable for indoor complex semantic query considering the computation of indoor spatial relations is provided According to the characteristics of indoor space. This paper designs ontology model describing the space related information of humans, events and Indoor space objects (e.g. Storey and Room) as well as their relations to meet the indoor semantic query. The ontology concepts are used in IndoorSPARQL query language which extends SPARQL syntax for representing and querying indoor space. And four types specific primitives for indoor query, "Adjacent", "Opposite", "Vertical" and "Contain", are defined as query functions in IndoorSPARQL used to support quantitative spatial computations. Also a method is proposed to analysis the query language. Finally this paper adopts this method to realize indoor semantic query on the study area through constructing the ontology model for the study building. The experimental results show that the method proposed in this paper can effectively support complex indoor space semantic query.
Age of Acquisition Effects on Word Processing for Chinese Native Learners’ English: ERP Evidence for the Arbitrary Mapping Hypothesis

PubMed Central

Xue, Jin; Liu, Tongtong; Marmolejo-Ramos, Fernando; Pei, Xuna

2017-01-01

The present study aimed at distinguishing processing of early learned L2 words from late ones for Chinese natives who learn English as a foreign language. Specifically, we examined whether the age of acquisition (AoA) effect arose during the arbitrary mapping from conceptual knowledge onto linguistic units. The behavior and ERP data were collected when 28 Chinese-English bilinguals were asked to perform semantic relatedness judgment on word pairs, which represented three stages of word learning (i.e., primary school, junior and senior high schools). A 3 (AoA: early vs. intermediate vs. late) × 2 (regularity: regular vs. irregular) × 2 (semantic relatedness: related vs. unrelated) × 2 (hemisphere: left vs. right) × 3 (brain area: anterior vs. central vs. posterior) within-subjects design was adopted. Results from the analysis of N100 and N400 amplitudes showed that early learned words had an advantage in processing accuracy and speed; there is a tendency that the AoA effect was more pronounced for irregular word pairs and in the semantic related condition. More important, ERP results showed early acquired words induced larger N100 amplitudes for early AoA words in the parietal area and more negative-going N400 than late acquire words in the frontal and central regions. The results indicate the locus of the AoA effect might derive from the arbitrary mapping between word forms and semantic concepts, and early acquired words have more semantic interconnections than late acquired words. PMID:28572785
Realizing Organizational Collaboration through Semantic Mediation

DTIC Science & Technology

2008-05-21

As with any community-driven effort, building consensus across a wide range of stakeholders is generally a slow and politically driven process...25] Jena, http://jena.sourceforge.net/ [26] Saxon, http://saxon.sourceforge.net/ [27] Oussama Kassem Zein and Yvon Kermarrec, “An Approach for
Eye Movements and Visual Memory for Scenes

DTIC Science & Technology

2005-01-01

Scene memory research has demonstrated that the memory representation of a semantically inconsistent object in a scene is more detailed and/or complete... memory during scene viewing, then changes to semantically inconsistent objects (which should be represented more com- pletely) should be detected more... semantic description. Due to the surprise nature of the visual memory test, any learning that occurred during the search portion of the experiment was
Development of Lexical-Semantic Language System: N400 Priming Effect for Spoken Words in 18- and 24-Month Old Children

ERIC Educational Resources Information Center

Rama, Pia; Sirri, Louah; Serres, Josette

2013-01-01

Our aim was to investigate whether developing language system, as measured by a priming task for spoken words, is organized by semantic categories. Event-related potentials (ERPs) were recorded during a priming task for spoken words in 18- and 24-month-old monolingual French learning children. Spoken word pairs were either semantically related…
Relearning in Semantic Dementia Reflects Contributions from Both Medial Temporal Lobe Episodic and Degraded Neocortical Semantic Systems: Evidence in Support of the Complementary Learning Systems Theory

ERIC Educational Resources Information Center

Mayberry, Emily J.; Sage, Karen; Ehsan, Sheeba; Ralph, Matthew A. Lambon

2011-01-01

When relearning words, patients with semantic dementia (SD) exhibit a characteristic rigidity, including a failure to generalise names to untrained exemplars of trained concepts. This has been attributed to an over-reliance on the medial temporal region which captures information in sparse, non-overlapping and therefore rigid representations. The…
Populating the Semantic Web by Macro-reading Internet Text

NASA Astrophysics Data System (ADS)

Mitchell, Tom M.; Betteridge, Justin; Carlson, Andrew; Hruschka, Estevam; Wang, Richard

A key question regarding the future of the semantic web is "how will we acquire structured information to populate the semantic web on a vast scale?" One approach is to enter this information manually. A second approach is to take advantage of pre-existing databases, and to develop common ontologies, publishing standards, and reward systems to make this data widely accessible. We consider here a third approach: developing software that automatically extracts structured information from unstructured text present on the web. We also describe preliminary results demonstrating that machine learning algorithms can learn to extract tens of thousands of facts to populate a diverse ontology, with imperfect but reasonably good accuracy.
LEARNING SEMANTICS-ENHANCED LANGUAGE MODELS APPLIED TO UNSUEPRVISED WSD

DOE Office of Scientific and Technical Information (OSTI.GOV)

VERSPOOR, KARIN; LIN, SHOU-DE

An N-gram language model aims at capturing statistical syntactic word order information from corpora. Although the concept of language models has been applied extensively to handle a variety of NLP problems with reasonable success, the standard model does not incorporate semantic information, and consequently limits its applicability to semantic problems such as word sense disambiguation. We propose a framework that integrates semantic information into the language model schema, allowing a system to exploit both syntactic and semantic information to address NLP problems. Furthermore, acknowledging the limited availability of semantically annotated data, we discuss how the proposed model can be learnedmore » without annotated training examples. Finally, we report on a case study showing how the semantics-enhanced language model can be applied to unsupervised word sense disambiguation with promising results.« less
Fast mapping semantic features: performance of adults with normal language, history of disorders of spoken and written language, and attention deficit hyperactivity disorder on a word-learning task.

PubMed

Alt, Mary; Gutmann, Michelle L

2009-01-01

This study was designed to test the word learning abilities of adults with typical language abilities, those with a history of disorders of spoken or written language (hDSWL), and hDSWL plus attention deficit hyperactivity disorder (+ADHD). Sixty-eight adults were required to associate a novel object with a novel label, and then recognize semantic features of the object and phonological features of the label. Participants were tested for overt ability (accuracy) and covert processing (reaction time). The +ADHD group was less accurate at mapping semantic features and slower to respond to lexical labels than both other groups. Different factors correlated with word learning performance for each group. Adults with language and attention deficits are more impaired at word learning than adults with language deficits only. Despite behavioral profiles like typical peers, adults with hDSWL may use different processing strategies than their peers. Readers will be able to: (1) recognize the influence of a dual disability (hDSWL and ADHD) on word learning outcomes; (2) identify factors that may contribute to word learning in adults in terms of (a) the nature of the words to be learned and (b) the language processing of the learner.

Variation across individuals and items determine learning outcomes from fast mapping.

PubMed

Coutanche, Marc N; Koch, Griffin E

2017-11-01

An approach to learning words known as "fast mapping" has been linked to unique neurobiological and behavioral markers in adult humans, including rapid lexical integration. However, the mechanisms supporting fast mapping are still not known. In this study, we sought to help change this by examining factors that modulate learning outcomes. In 90 subjects, we systematically manipulated the typicality of the items used to support fast mapping (foils), and quantified learners' inclination to employ semantic, episodic, and spatial memory through the Survey of Autobiographical Memory (SAM). We asked how these factors affect lexical competition and recognition performance, and then asked how foil typicality and lexical competition are related in an independent dataset. We find that both the typicality of fast mapping foils, and individual differences in how different memory systems are employed, influence lexical competition effects after fast mapping, but not after other learning approaches. Specifically, learning a word through fast mapping with an atypical foil led to lexical competition, while a typical foil led to lexical facilitation. This effect was particularly evident in individuals with a strong tendency to employ semantic memory. We further replicated the relationship between continuous foil atypicality and lexical competition in an independent dataset. These findings suggest that semantic properties of the foils that support fast mapping can influence the degree and nature of subsequent lexical integration. Further, the effects of foils differ based on an individual's tendency to draw-on the semantic memory system. Copyright © 2017 Elsevier Ltd. All rights reserved.
Lexical Quality in the Brain: ERP evidence for robust word learning from context

PubMed Central

Frishkoff, Gwen A.; Perfetti, Charles A.; Collins-Thompson, K

2010-01-01

We examined event-related potentials (ERPs) before and after word learning, using training contexts that differed in their level of contextual support for meaning acquisition. Novel words appeared either in contexts that were semantically constraining, providing strong cues to meaning, or in contexts that were weakly constraining, that is, uninformative. After each sentence, participants were shown the word in isolation and were asked to generate a close synonym. Immediately after training, words trained in high-constraint contexts elicited a smaller left temporal negativity (N300FT7) compared with words trained in low-constraint contexts, and both types of trained words elicited a stronger medial frontal negativity (N350Fz) relative to familiar words. Two days after training the N300FT7 disappeared and was replaced by a later, left parietal (P600Pz) effect. To examine robust learning, we administered a semantic priming test two days after training. Familiar words and words trained in high-constraint contexts elicited strong N400 effects. By contrast, words trained in low-constraint contexts elicited a weak N400 effect, and novel (untrained rare) words elicited no semantic priming. These findings suggest that supportive contexts and the use of an active meaning-generation task may lead to robust word learning. The effects of this training can be observed as changes in an early left frontal component, as well as the classical N400 effect. We discuss implications for theories of "partial" semantic knowledge and for robust word learning and instruction. PMID:20614356
Semantic Maps Capturing Organization Knowledge in e-Learning

NASA Astrophysics Data System (ADS)

Mavridis, Androklis; Koumpis, Adamantios; Demetriadis, Stavros N.

e-learning, shows much promise in accessibility and opportunity to learn, due to its asynchronous nature and its ability to transmit knowledge fast and effectively. However without a universal standard for online learning and teaching, many systems are proclaimed as “e-learning-compliant”, offering nothing more than automated services for delivering courses online, providing no additional enhancement to reusability and learner personalization. Hence, the focus is not on providing reusable and learner-centered content, but on developing the technology aspects of e-learning. This current trend has made it crucial to find a more refined definition of what constitutes knowledge in the e-learning context. We propose an e-learning system architecture that makes use of a knowledge model to facilitate continuous dialogue and inquiry-based knowledge learning, by exploiting the full benefits of the semantic web as a medium capable for supplying the web with formalized knowledge.
The BioIntelligence Framework: a new computational platform for biomedical knowledge computing.

PubMed

Farley, Toni; Kiefer, Jeff; Lee, Preston; Von Hoff, Daniel; Trent, Jeffrey M; Colbourn, Charles; Mousses, Spyro

2013-01-01

Breakthroughs in molecular profiling technologies are enabling a new data-intensive approach to biomedical research, with the potential to revolutionize how we study, manage, and treat complex diseases. The next great challenge for clinical applications of these innovations will be to create scalable computational solutions for intelligently linking complex biomedical patient data to clinically actionable knowledge. Traditional database management systems (DBMS) are not well suited to representing complex syntactic and semantic relationships in unstructured biomedical information, introducing barriers to realizing such solutions. We propose a scalable computational framework for addressing this need, which leverages a hypergraph-based data model and query language that may be better suited for representing complex multi-lateral, multi-scalar, and multi-dimensional relationships. We also discuss how this framework can be used to create rapid learning knowledge base systems to intelligently capture and relate complex patient data to biomedical knowledge in order to automate the recovery of clinically actionable information.
Sharing One Biographical Detail Elicits Priming between Famous Names: Empirical and Computational Approaches

PubMed Central

Ihrke, Matthias; Brennen, Tim

2011-01-01

In this paper three experiments and corresponding model simulations are reported that investigate the priming of famous name recognition in order to explore the structure of the part of the semantic system dealing with people. Consistent with empirical findings, novel computational simulations using Burton et al.’s interactive activation and competition model point to a conceptual distinction between how priming is initiated in single- and double-familiarity tasks, indicating that priming should be weaker or non-existent for the single-familiarity task. Experiment 1 demonstrates that, within a double-familiarity framework using famous names, categorical, and associative priming are reliable effects. Pushing the model to the limit, it predicts that pairs of celebrities who are neither associatively nor categorically related but who share single biographical features, both died in a car crash for example, should prime each other. Experiment 2 investigated this in a double-familiarity task but the effect was not observed. We therefore simulated and realized a pairwise learning task that was conceptually similar to the double-familiarity-decision task but allowed to strengthen the underlying connections. Priming based on a single biographical feature could be found both in simulations and the experiment. The effect was not due to visual or name similarity which were controlled for and participants did not report using the biographical links between the people to learn the pairs. The results are interpreted to lend further support to structural models of the memory for persons. Furthermore, the results are consistent with the idea that episodic features known about people are stored in semantic memory and are automatically activated when encountering that person. PMID:21687446
Automatic Scoring of Multiple Semantic Attributes With Multi-Task Feature Leverage: A Study on Pulmonary Nodules in CT Images.

PubMed

Sihong Chen; Jing Qin; Xing Ji; Baiying Lei; Tianfu Wang; Dong Ni; Jie-Zhi Cheng

2017-03-01

The gap between the computational and semantic features is the one of major factors that bottlenecks the computer-aided diagnosis (CAD) performance from clinical usage. To bridge this gap, we exploit three multi-task learning (MTL) schemes to leverage heterogeneous computational features derived from deep learning models of stacked denoising autoencoder (SDAE) and convolutional neural network (CNN), as well as hand-crafted Haar-like and HoG features, for the description of 9 semantic features for lung nodules in CT images. We regard that there may exist relations among the semantic features of "spiculation", "texture", "margin", etc., that can be explored with the MTL. The Lung Image Database Consortium (LIDC) data is adopted in this study for the rich annotation resources. The LIDC nodules were quantitatively scored w.r.t. 9 semantic features from 12 radiologists of several institutes in U.S.A. By treating each semantic feature as an individual task, the MTL schemes select and map the heterogeneous computational features toward the radiologists' ratings with cross validation evaluation schemes on the randomly selected 2400 nodules from the LIDC dataset. The experimental results suggest that the predicted semantic scores from the three MTL schemes are closer to the radiologists' ratings than the scores from single-task LASSO and elastic net regression methods. The proposed semantic attribute scoring scheme may provide richer quantitative assessments of nodules for better support of diagnostic decision and management. Meanwhile, the capability of the automatic association of medical image contents with the clinical semantic terms by our method may also assist the development of medical search engine.
Developmental amnesia: Fractionation of developing memory systems.

PubMed

Temple, Christine M; Richardson, Paul

2006-07-01

Study of the developmental amnesias utilizing a cognitive neuropsychological methodology has highlighted the dissociations that may occur between the development of components of memory. M.M., a new case of developmental amnesia, was identified after screening from the normal population on cognitive and memory measures. Retrospective investigation found that he was of low birthweight. M.M. had impaired semantic memory for knowledge of facts and words. There was impaired episodic memory for words and stories but intact episodic memory for visual designs and features. This forms a double dissociation with Dr S. (Temple, 1992), who had intact verbal but impaired visual episodic memory. M.M. also had impaired autobiographical episodic memory. Nevertheless, learning over repeated trials occurred, consistent with previous theorizing that learning is not simply the effect of recurrent episodic memory. Nor is it the same as establishing semantic memory, since for M.M. semantic memory is also impaired. Within reading, there was an impaired lexico-semantic system, elevated levels of homophone confusion, but intact phonological reading, consistent with surface dyslexia and raising issues about the interrelationship of the semantic system and literacy development. The results are compatible with discrete semi-independent components within memory development, whereby deficits are associated with residual normality, but there may also be an explicit relationship between the semantic memory system and both vocabulary and reading acquisition.
Lexical learning in mild aphasia: gesture benefit depends on patholinguistic profile and lesion pattern.

PubMed

Kroenke, Klaus-Martin; Kraft, Indra; Regenbrecht, Frank; Obrig, Hellmuth

2013-01-01

Gestures accompany speech and enrich human communication. When aphasia interferes with verbal abilities, gestures become even more relevant, compensating for and/or facilitating verbal communication. However, small-scale clinical studies yielded diverging results with regard to a therapeutic gesture benefit for lexical retrieval. Based on recent functional neuroimaging results, delineating a speech-gesture integration network for lexical learning in healthy adults, we hypothesized that the commonly observed variability may stem from differential patholinguistic profiles in turn depending on lesion pattern. Therefore we used a controlled novel word learning paradigm to probe the impact of gestures on lexical learning, in the lesioned language network. Fourteen patients with chronic left hemispheric lesions and mild residual aphasia learned 30 novel words for manipulable objects over four days. Half of the words were trained with gestures while the other half were trained purely verbally. For the gesture condition, rootwords were visually presented (e.g., Klavier, [piano]), followed by videos of the corresponding gestures and the auditory presentation of the novel words (e.g., /krulo/). Participants had to repeat pseudowords and simultaneously reproduce gestures. In the verbal condition no gesture-video was shown and participants only repeated pseudowords orally. Correlational analyses confirmed that gesture benefit depends on the patholinguistic profile: lesser lexico-semantic impairment correlated with better gesture-enhanced learning. Conversely largely preserved segmental-phonological capabilities correlated with better purely verbal learning. Moreover, structural MRI-analysis disclosed differential lesion patterns, most interestingly suggesting that integrity of the left anterior temporal pole predicted gesture benefit. Thus largely preserved semantic capabilities and relative integrity of a semantic integration network are prerequisites for successful use of the multimodal learning strategy, in which gestures may cause a deeper semantic rooting of the novel word-form. The results tap into theoretical accounts of gestures in lexical learning and suggest an explanation for the diverging effect in therapeutical studies advocating gestures in aphasia rehabilitation. Copyright © 2013 Elsevier Ltd. All rights reserved.
Adaptive learning in a compartmental model of visual cortex—how feedback enables stable category learning and refinement

PubMed Central

Layher, Georg; Schrodt, Fabian; Butz, Martin V.; Neumann, Heiko

2014-01-01

The categorization of real world objects is often reflected in the similarity of their visual appearances. Such categories of objects do not necessarily form disjunct sets of objects, neither semantically nor visually. The relationship between categories can often be described in terms of a hierarchical structure. For instance, tigers and leopards build two separate mammalian categories, both of which are subcategories of the category Felidae. In the last decades, the unsupervised learning of categories of visual input stimuli has been addressed by numerous approaches in machine learning as well as in computational neuroscience. However, the question of what kind of mechanisms might be involved in the process of subcategory learning, or category refinement, remains a topic of active investigation. We propose a recurrent computational network architecture for the unsupervised learning of categorial and subcategorial visual input representations. During learning, the connection strengths of bottom-up weights from input to higher-level category representations are adapted according to the input activity distribution. In a similar manner, top-down weights learn to encode the characteristics of a specific stimulus category. Feedforward and feedback learning in combination realize an associative memory mechanism, enabling the selective top-down propagation of a category's feedback weight distribution. We suggest that the difference between the expected input encoded in the projective field of a category node and the current input pattern controls the amplification of feedforward-driven representations. Large enough differences trigger the recruitment of new representational resources and the establishment of additional (sub-) category representations. We demonstrate the temporal evolution of such learning and show how the proposed combination of an associative memory with a modulatory feedback integration successfully establishes category and subcategory representations. PMID:25538637
Exemplar-Based Image and Video Stylization Using Fully Convolutional Semantic Features.

PubMed

Zhu, Feida; Yan, Zhicheng; Bu, Jiajun; Yu, Yizhou

2017-05-10

Color and tone stylization in images and videos strives to enhance unique themes with artistic color and tone adjustments. It has a broad range of applications from professional image postprocessing to photo sharing over social networks. Mainstream photo enhancement softwares, such as Adobe Lightroom and Instagram, provide users with predefined styles, which are often hand-crafted through a trial-and-error process. Such photo adjustment tools lack a semantic understanding of image contents and the resulting global color transform limits the range of artistic styles it can represent. On the other hand, stylistic enhancement needs to apply distinct adjustments to various semantic regions. Such an ability enables a broader range of visual styles. In this paper, we first propose a novel deep learning architecture for exemplar-based image stylization, which learns local enhancement styles from image pairs. Our deep learning architecture consists of fully convolutional networks (FCNs) for automatic semantics-aware feature extraction and fully connected neural layers for adjustment prediction. Image stylization can be efficiently accomplished with a single forward pass through our deep network. To extend our deep network from image stylization to video stylization, we exploit temporal superpixels (TSPs) to facilitate the transfer of artistic styles from image exemplars to videos. Experiments on a number of datasets for image stylization as well as a diverse set of video clips demonstrate the effectiveness of our deep learning architecture.
Survey on Ontology Mapping

NASA Astrophysics Data System (ADS)

Zhu, Junwu

To create a sharable semantic space in which the terms from different domain ontology or knowledge system, Ontology mapping become a hot research point in Semantic Web Community. In this paper, motivated factors of ontology mapping research are given firstly, and then 5 dominating theories and methods, such as information accessing technology, machine learning, linguistics, structure graph and similarity, are illustrated according their technology class. Before we analyses the new requirements and takes a long view, the contributions of these theories and methods are summarized in details. At last, this paper suggest to design a group of semantic connector with the ability of migration learning for OWL-2 extended with constrains and the ontology mapping theory of axiom, so as to provide a new methodology for ontology mapping.
Deep visual-semantic for crowded video understanding

NASA Astrophysics Data System (ADS)

Deng, Chunhua; Zhang, Junwen

2018-03-01

Visual-semantic features play a vital role for crowded video understanding. Convolutional Neural Networks (CNNs) have experienced a significant breakthrough in learning representations from images. However, the learning of visualsemantic features, and how it can be effectively extracted for video analysis, still remains a challenging task. In this study, we propose a novel visual-semantic method to capture both appearance and dynamic representations. In particular, we propose a spatial context method, based on the fractional Fisher vector (FV) encoding on CNN features, which can be regarded as our main contribution. In addition, to capture temporal context information, we also applied fractional encoding method on dynamic images. Experimental results on the WWW crowed video dataset demonstrate that the proposed method outperform the state of the art.
Alignment of the UMLS semantic network with BioTop: methodology and assessment.

PubMed

Schulz, Stefan; Beisswanger, Elena; van den Hoek, László; Bodenreider, Olivier; van Mulligen, Erik M

2009-06-15

For many years, the Unified Medical Language System (UMLS) semantic network (SN) has been used as an upper-level semantic framework for the categorization of terms from terminological resources in biomedicine. BioTop has recently been developed as an upper-level ontology for the biomedical domain. In contrast to the SN, it is founded upon strict ontological principles, using OWL DL as a formal representation language, which has become standard in the semantic Web. In order to make logic-based reasoning available for the resources annotated or categorized with the SN, a mapping ontology was developed aligning the SN with BioTop. The theoretical foundations and the practical realization of the alignment are being described, with a focus on the design decisions taken, the problems encountered and the adaptations of BioTop that became necessary. For evaluation purposes, UMLS concept pairs obtained from MEDLINE abstracts by a named entity recognition system were tested for possible semantic relationships. Furthermore, all semantic-type combinations that occur in the UMLS Metathesaurus were checked for satisfiability. The effort-intensive alignment process required major design changes and enhancements of BioTop and brought up several design errors that could be fixed. A comparison between a human curator and the ontology yielded only a low agreement. Ontology reasoning was also used to successfully identify 133 inconsistent semantic-type combinations. BioTop, the OWL DL representation of the UMLS SN, and the mapping ontology are available at http://www.purl.org/biotop/.
The Construction of Semantic Memory: Grammar-Based Representations Learned from Relational Episodic Information

PubMed Central

Battaglia, Francesco P.; Pennartz, Cyriel M. A.

2011-01-01

After acquisition, memories underlie a process of consolidation, making them more resistant to interference and brain injury. Memory consolidation involves systems-level interactions, most importantly between the hippocampus and associated structures, which takes part in the initial encoding of memory, and the neocortex, which supports long-term storage. This dichotomy parallels the contrast between episodic memory (tied to the hippocampal formation), collecting an autobiographical stream of experiences, and semantic memory, a repertoire of facts and statistical regularities about the world, involving the neocortex at large. Experimental evidence points to a gradual transformation of memories, following encoding, from an episodic to a semantic character. This may require an exchange of information between different memory modules during inactive periods. We propose a theory for such interactions and for the formation of semantic memory, in which episodic memory is encoded as relational data. Semantic memory is modeled as a modified stochastic grammar, which learns to parse episodic configurations expressed as an association matrix. The grammar produces tree-like representations of episodes, describing the relationships between its main constituents at multiple levels of categorization, based on its current knowledge of world regularities. These regularities are learned by the grammar from episodic memory information, through an expectation-maximization procedure, analogous to the inside–outside algorithm for stochastic context-free grammars. We propose that a Monte-Carlo sampling version of this algorithm can be mapped on the dynamics of “sleep replay” of previously acquired information in the hippocampus and neocortex. We propose that the model can reproduce several properties of semantic memory such as decontextualization, top-down processing, and creation of schemata. PMID:21887143
Client-Side Event Processing for Personalized Web Advertisement

NASA Astrophysics Data System (ADS)

Stühmer, Roland; Anicic, Darko; Sen, Sinan; Ma, Jun; Schmidt, Kay-Uwe; Stojanovic, Nenad

The market for Web advertisement is continuously growing and correspondingly, the number of approaches that can be used for realizing Web advertisement are increasing. However, current approaches fail to generate very personalized ads for a current Web user that is visiting a particular Web content. They mainly try to develop a profile based on the content of that Web page or on a long-term user's profile, by not taking into account current user's preferences. We argue that by discovering a user's interest from his current Web behavior we can support the process of ad generation, especially the relevance of an ad for the user. In this paper we present the conceptual architecture and implementation of such an approach. The approach is based on the extraction of simple events from the user interaction with a Web page and their combination in order to discover the user's interests. We use semantic technologies in order to build such an interpretation out of many simple events. We present results from preliminary evaluation studies. The main contribution of the paper is a very efficient, semantic-based client-side architecture for generating and combining Web events. The architecture ensures the agility of the whole advertisement system, by complexly processing events on the client. In general, this work contributes to the realization of new, event-driven applications for the (Semantic) Web.
The effects of late acquisition of L2 and the consequences of immigration on L1 for semantic and morpho-syntactic language aspects.

PubMed

Scherag, André; Demuth, Lisa; Rösler, Frank; Neville, Helen J; Röder, Brigitte

2004-10-01

It has been hypothesized that some aspects of a second language (L2) might be learned easier than others if a language is learned late. On the other hand, non-use might result in a loss of language skills in one's native, i.e. one's first language (L1) (language attrition). To study which, if any, aspects of language are affected by either late acquisition or non-use, long-term German immigrants to the US and English native speakers who are long-term immigrants to Germany as well as two additional control groups of native German speakers were tested with an auditory semantic and morpho-syntactic priming paradigm. German adjectives correctly or incorrectly inflected for gender and semantically associated or not associated with the target noun served as primes. Participants made a lexical decision on the target word. All groups of native German speakers gained from semantically and morpho-syntactically congruent primes. Evidence for language attrition was neither found for semantic nor morpho-syntactic priming effects in the German immigrants. In contrast, English native speakers did not gain from a morpho-syntactic congruent prime, whereas semantic priming effects were similar as for the remaining groups. The present data suggest that the full acquisition of at least some syntactic functions may be restricted to limited periods in life while semantic and morpho-syntactic functions seem to be relatively inured to loss due to non-use.
Learning for Semantic Parsing with Kernels under Various Forms of Supervision

DTIC Science & Technology

2007-08-01

natural language sentences to their formal executable meaning representations. This is a challenging problem and is critical for developing computing...sentences are semantically tractable. This indi- cates that Geoquery is more challenging domain for semantic parsing than ATIS. In the past, there have been a...Combining parsers. In Proceedings of the Conference on Em- pirical Methods in Natural Language Processing and Very Large Corpora (EMNLP/ VLC -99), pp. 187–194
KOJAK: Scalable Semantic Link Discovery Via Integrated Knowledge-Based and Statistical Reasoning

DTIC Science & Technology

2006-11-01

program can find interesting connections in a network without having to learn the patterns of interestingness beforehand. The key advantage of our...Interesting Instances in Semantic Graphs Below we describe how the UNICORN framework can discover interesting instances in a multi-relational dataset...We can now describe how UNICORN solves the first problem of finding the top interesting nodes in a semantic net by ranking them according to
The Yin and the Yang of Prediction: An fMRI Study of Semantic Predictive Processing

PubMed Central

Weber, Kirsten; Lau, Ellen F.; Stillerman, Benjamin; Kuperberg, Gina R.

2016-01-01

Probabilistic prediction plays a crucial role in language comprehension. When predictions are fulfilled, the resulting facilitation allows for fast, efficient processing of ambiguous, rapidly-unfolding input; when predictions are not fulfilled, the resulting error signal allows us to adapt to broader statistical changes in this input. We used functional Magnetic Resonance Imaging to examine the neuroanatomical networks engaged in semantic predictive processing and adaptation. We used a relatedness proportion semantic priming paradigm, in which we manipulated the probability of predictions while holding local semantic context constant. Under conditions of higher (versus lower) predictive validity, we replicate previous observations of reduced activity to semantically predictable words in the left anterior superior/middle temporal cortex, reflecting facilitated processing of targets that are consistent with prior semantic predictions. In addition, under conditions of higher (versus lower) predictive validity we observed significant differences in the effects of semantic relatedness within the left inferior frontal gyrus and the posterior portion of the left superior/middle temporal gyrus. We suggest that together these two regions mediated the suppression of unfulfilled semantic predictions and lexico-semantic processing of unrelated targets that were inconsistent with these predictions. Moreover, under conditions of higher (versus lower) predictive validity, a functional connectivity analysis showed that the left inferior frontal and left posterior superior/middle temporal gyrus were more tightly interconnected with one another, as well as with the left anterior cingulate cortex. The left anterior cingulate cortex was, in turn, more tightly connected to superior lateral frontal cortices and subcortical regions—a network that mediates rapid learning and adaptation and that may have played a role in switching to a more predictive mode of processing in response to the statistical structure of the wider environmental context. Together, these findings highlight close links between the networks mediating semantic prediction, executive function and learning, giving new insights into how our brains are able to flexibly adapt to our environment. PMID:27010386
The Yin and the Yang of Prediction: An fMRI Study of Semantic Predictive Processing.

PubMed

Weber, Kirsten; Lau, Ellen F; Stillerman, Benjamin; Kuperberg, Gina R

2016-01-01

Probabilistic prediction plays a crucial role in language comprehension. When predictions are fulfilled, the resulting facilitation allows for fast, efficient processing of ambiguous, rapidly-unfolding input; when predictions are not fulfilled, the resulting error signal allows us to adapt to broader statistical changes in this input. We used functional Magnetic Resonance Imaging to examine the neuroanatomical networks engaged in semantic predictive processing and adaptation. We used a relatedness proportion semantic priming paradigm, in which we manipulated the probability of predictions while holding local semantic context constant. Under conditions of higher (versus lower) predictive validity, we replicate previous observations of reduced activity to semantically predictable words in the left anterior superior/middle temporal cortex, reflecting facilitated processing of targets that are consistent with prior semantic predictions. In addition, under conditions of higher (versus lower) predictive validity we observed significant differences in the effects of semantic relatedness within the left inferior frontal gyrus and the posterior portion of the left superior/middle temporal gyrus. We suggest that together these two regions mediated the suppression of unfulfilled semantic predictions and lexico-semantic processing of unrelated targets that were inconsistent with these predictions. Moreover, under conditions of higher (versus lower) predictive validity, a functional connectivity analysis showed that the left inferior frontal and left posterior superior/middle temporal gyrus were more tightly interconnected with one another, as well as with the left anterior cingulate cortex. The left anterior cingulate cortex was, in turn, more tightly connected to superior lateral frontal cortices and subcortical regions-a network that mediates rapid learning and adaptation and that may have played a role in switching to a more predictive mode of processing in response to the statistical structure of the wider environmental context. Together, these findings highlight close links between the networks mediating semantic prediction, executive function and learning, giving new insights into how our brains are able to flexibly adapt to our environment.

Brain connections of words, perceptions and actions: A neurobiological model of spatio-temporal semantic activation in the human cortex.

PubMed

Tomasello, Rosario; Garagnani, Max; Wennekers, Thomas; Pulvermüller, Friedemann

2017-04-01

Neuroimaging and patient studies show that different areas of cortex respectively specialize for general and selective, or category-specific, semantic processing. Why are there both semantic hubs and category-specificity, and how come that they emerge in different cortical regions? Can the activation time-course of these areas be predicted and explained by brain-like network models? In this present work, we extend a neurocomputational model of human cortical function to simulate the time-course of cortical processes of understanding meaningful concrete words. The model implements frontal and temporal cortical areas for language, perception, and action along with their connectivity. It uses Hebbian learning to semantically ground words in aspects of their referential object- and action-related meaning. Compared with earlier proposals, the present model incorporates additional neuroanatomical links supported by connectivity studies and downscaled synaptic weights in order to control for functional between-area differences purely due to the number of in- or output links of an area. We show that learning of semantic relationships between words and the objects and actions these symbols are used to speak about, leads to the formation of distributed circuits, which all include neuronal material in connector hub areas bridging between sensory and motor cortical systems. Therefore, these connector hub areas acquire a role as semantic hubs. By differentially reaching into motor or visual areas, the cortical distributions of the emergent 'semantic circuits' reflect aspects of the represented symbols' meaning, thus explaining category-specificity. The improved connectivity structure of our model entails a degree of category-specificity even in the 'semantic hubs' of the model. The relative time-course of activation of these areas is typically fast and near-simultaneous, with semantic hubs central to the network structure activating before modality-preferential areas carrying semantic information. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.
Pervasive Knowledge, Social Networks, and Cloud Computing: E-Learning 2.0

ERIC Educational Resources Information Center

Anshari, Muhammad; Alas, Yabit; Guan, Lim Sei

2015-01-01

Embedding Web 2.0 in learning processes has extended learning from traditional based learning-centred to a collaborative based learning-centred institution that emphasises learning anywhere and anytime. While deploying Semantic Web into e-learning offers a broader spectrum of pervasive knowledge acquisition to enrich users' experience in learning.…
Learning Semantic Tags from Big Data for Clinical Text Representation.

PubMed

Li, Yanpeng; Liu, Hongfang

2015-01-01

In clinical text mining, it is one of the biggest challenges to represent medical terminologies and n-gram terms in sparse medical reports using either supervised or unsupervised methods. Addressing this issue, we propose a novel method for word and n-gram representation at semantic level. We first represent each word by its distance with a set of reference features calculated by reference distance estimator (RDE) learned from labeled and unlabeled data, and then generate new features using simple techniques of discretization, random sampling and merging. The new features are a set of binary rules that can be interpreted as semantic tags derived from word and n-grams. We show that the new features significantly outperform classical bag-of-words and n-grams in the task of heart disease risk factor extraction in i2b2 2014 challenge. It is promising to see that semantics tags can be used to replace the original text entirely with even better prediction performance as well as derive new rules beyond lexical level.
A learning perspective on individual differences in skilled reading: Exploring and exploiting orthographic and semantic discrimination cues.

PubMed

Milin, Petar; Divjak, Dagmar; Baayen, R Harald

2017-11-01

The goal of the present study is to understand the role orthographic and semantic information play in the behavior of skilled readers. Reading latencies from a self-paced sentence reading experiment in which Russian near-synonymous verbs were manipulated appear well-predicted by a combination of bottom-up sublexical letter triplets (trigraphs) and top-down semantic generalizations, modeled using the Naive Discrimination Learner. The results reveal a complex interplay of bottom-up and top-down support from orthography and semantics to the target verbs, whereby activations from orthography only are modulated by individual differences. Using performance on a serial reaction time (SRT) task for a novel operationalization of the mental speed hypothesis, we explain the observed individual differences in reading behavior in terms of the exploration/exploitation hypothesis from reinforcement learning, where initially slower and more variable behavior leads to better performance overall. (PsycINFO Database Record (c) 2017 APA, all rights reserved).
The semantic representation of prejudice and stereotypes.

PubMed

Bhatia, Sudeep

2017-07-01

We use a theory of semantic representation to study prejudice and stereotyping. Particularly, we consider large datasets of newspaper articles published in the United States, and apply latent semantic analysis (LSA), a prominent model of human semantic memory, to these datasets to learn representations for common male and female, White, African American, and Latino names. LSA performs a singular value decomposition on word distribution statistics in order to recover word vector representations, and we find that our recovered representations display the types of biases observed in human participants using tasks such as the implicit association test. Importantly, these biases are strongest for vector representations with moderate dimensionality, and weaken or disappear for representations with very high or very low dimensionality. Moderate dimensional LSA models are also the best at learning race, ethnicity, and gender-based categories, suggesting that social category knowledge, acquired through dimensionality reduction on word distribution statistics, can facilitate prejudiced and stereotyped associations. Copyright © 2017 Elsevier B.V. All rights reserved.
Using Wikipedia to learn semantic feature representations of concrete concepts in neuroimaging experiments

PubMed Central

Pereira, Francisco; Botvinick, Matthew; Detre, Greg

2012-01-01

In this paper we show that a corpus of a few thousand Wikipedia articles about concrete or visualizable concepts can be used to produce a low-dimensional semantic feature representation of those concepts. The purpose of such a representation is to serve as a model of the mental context of a subject during functional magnetic resonance imaging (fMRI) experiments. A recent study [19] showed that it was possible to predict fMRI data acquired while subjects thought about a concrete concept, given a representation of those concepts in terms of semantic features obtained with human supervision. We use topic models on our corpus to learn semantic features from text in an unsupervised manner, and show that those features can outperform those in [19] in demanding 12-way and 60-way classification tasks. We also show that these features can be used to uncover similarity relations in brain activation for different concepts which parallel those relations in behavioral data from human subjects. PMID:23243317
Automated Inspection of Power Line Corridors to Measure Vegetation Undercut Using Uav-Based Images

NASA Astrophysics Data System (ADS)

Maurer, M.; Hofer, M.; Fraundorfer, F.; Bischof, H.

2017-08-01

Power line corridor inspection is a time consuming task that is performed mostly manually. As the development of UAVs made huge progress in recent years, and photogrammetric computer vision systems became well established, it is time to further automate inspection tasks. In this paper we present an automated processing pipeline to inspect vegetation undercuts of power line corridors. For this, the area of inspection is reconstructed, geo-referenced, semantically segmented and inter class distance measurements are calculated. The presented pipeline performs an automated selection of the proper 3D reconstruction method for on the one hand wiry (power line), and on the other hand solid objects (surrounding). The automated selection is realized by performing pixel-wise semantic segmentation of the input images using a Fully Convolutional Neural Network. Due to the geo-referenced semantic 3D reconstructions a documentation of areas where maintenance work has to be performed is inherently included in the distance measurements and can be extracted easily. We evaluate the influence of the semantic segmentation according to the 3D reconstruction and show that the automated semantic separation in wiry and dense objects of the 3D reconstruction routine improves the quality of the vegetation undercut inspection. We show the generalization of the semantic segmentation to datasets acquired using different acquisition routines and to varied seasons in time.
Hierarchical Recurrent Neural Hashing for Image Retrieval With Hierarchical Convolutional Features.

PubMed

Lu, Xiaoqiang; Chen, Yaxiong; Li, Xuelong

Hashing has been an important and effective technology in image retrieval due to its computational efficiency and fast search speed. The traditional hashing methods usually learn hash functions to obtain binary codes by exploiting hand-crafted features, which cannot optimally represent the information of the sample. Recently, deep learning methods can achieve better performance, since deep learning architectures can learn more effective image representation features. However, these methods only use semantic features to generate hash codes by shallow projection but ignore texture details. In this paper, we proposed a novel hashing method, namely hierarchical recurrent neural hashing (HRNH), to exploit hierarchical recurrent neural network to generate effective hash codes. There are three contributions of this paper. First, a deep hashing method is proposed to extensively exploit both spatial details and semantic information, in which, we leverage hierarchical convolutional features to construct image pyramid representation. Second, our proposed deep network can exploit directly convolutional feature maps as input to preserve the spatial structure of convolutional feature maps. Finally, we propose a new loss function that considers the quantization error of binarizing the continuous embeddings into the discrete binary codes, and simultaneously maintains the semantic similarity and balanceable property of hash codes. Experimental results on four widely used data sets demonstrate that the proposed HRNH can achieve superior performance over other state-of-the-art hashing methods.Hashing has been an important and effective technology in image retrieval due to its computational efficiency and fast search speed. The traditional hashing methods usually learn hash functions to obtain binary codes by exploiting hand-crafted features, which cannot optimally represent the information of the sample. Recently, deep learning methods can achieve better performance, since deep learning architectures can learn more effective image representation features. However, these methods only use semantic features to generate hash codes by shallow projection but ignore texture details. In this paper, we proposed a novel hashing method, namely hierarchical recurrent neural hashing (HRNH), to exploit hierarchical recurrent neural network to generate effective hash codes. There are three contributions of this paper. First, a deep hashing method is proposed to extensively exploit both spatial details and semantic information, in which, we leverage hierarchical convolutional features to construct image pyramid representation. Second, our proposed deep network can exploit directly convolutional feature maps as input to preserve the spatial structure of convolutional feature maps. Finally, we propose a new loss function that considers the quantization error of binarizing the continuous embeddings into the discrete binary codes, and simultaneously maintains the semantic similarity and balanceable property of hash codes. Experimental results on four widely used data sets demonstrate that the proposed HRNH can achieve superior performance over other state-of-the-art hashing methods.
Intelligent Discovery for Learning Objects Using Semantic Web Technologies

ERIC Educational Resources Information Center

Hsu, I-Ching

2012-01-01

The concept of learning objects has been applied in the e-learning field to promote the accessibility, reusability, and interoperability of learning content. Learning Object Metadata (LOM) was developed to achieve these goals by describing learning objects in order to provide meaningful metadata. Unfortunately, the conventional LOM lacks the…
Information Structure and the Licensing of English Subjects

ERIC Educational Resources Information Center

Mack, Jennifer Elaine

2010-01-01

Most approaches to argument realization in English are grounded in lexical semantic structure. While it is widely acknowledged that there is an intimate relationship between information structure and grammatical relations such as "subject," there have been few attempts to formalize this observation. This dissertation proposes an "interface model…
The spacing effect in intentional and incidental free recall by children and adults: Limits on the automaticity hypothesis.

PubMed

Toppino, Thomas C; Fearnow-Kenney, Melodie D; Kiepert, Marissa H; Teremula, Amanda C

2009-04-01

Preschoolers, elementary school children, and college students exhibited a spacing effect in the free recall of pictures when learning was intentional. When learning was incidental and a shallow processing task requiring little semantic processing was used during list presentation, young adults still exhibited a spacing effect, but children consistently failed to do so. Children, however, did manifest a spacing effect in incidental learning when an elaborate semantic processing task was used. These results limit the hypothesis that the spacing effect in free recall occurs automatically and constrain theoretical accounts of why the spacing between repetitions affects recall performance.
LDRD final report :

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brost, Randolph C.; McLendon, William Clarence,

2013-01-01

Modeling geospatial information with semantic graphs enables search for sites of interest based on relationships between features, without requiring strong a priori models of feature shape or other intrinsic properties. Geospatial semantic graphs can be constructed from raw sensor data with suitable preprocessing to obtain a discretized representation. This report describes initial work toward extending geospatial semantic graphs to include temporal information, and initial results applying semantic graph techniques to SAR image data. We describe an efficient graph structure that includes geospatial and temporal information, which is designed to support simultaneous spatial and temporal search queries. We also report amore » preliminary implementation of feature recognition, semantic graph modeling, and graph search based on input SAR data. The report concludes with lessons learned and suggestions for future improvements.« less
Semantic Social Scaffolding for Capturing and Sharing Dissertation Experience

ERIC Educational Resources Information Center

Dimitrova, V.; Lau, L.; O'Rourke, R.

2011-01-01

This paper presents a novel collaborative tool--AWESOME Dissertation Environment (ADE)--which facilitates student learning through semantic social scaffolding: a new approach to dissertation writing challenges. These challenges revolve around three issues: timing of support; collective intelligence, and sense making strategies in tension with the…
Auto-Relevancy Baseline: A Hybrid System Without Human Feedback

DTIC Science & Technology

2010-11-01

classical Bayes algorithm upon the pseudo-hybridization of SemanticA and Latent Semantic IndexingBC systems should smooth out historically high yet...black box emulated a machine learning topic expert. Similar to some Web methods, the initial topics within the legal document were expanded upon
Semantic Processing for Communicative Exercises in Foreign-Language Learning.

ERIC Educational Resources Information Center

Mulford, George W.

1989-01-01

Outlines the history of semantically based programs that have influenced the design of computer assisted language instruction (CALI) programs. Describes early attempts to make intelligent CALI as well as current projects, including the Foreign Language Adventure Game, developed at the University of Delaware. Describes some important…
Learning for Semantic Parsing and Natural Language Generation Using Statistical Machine Translation Techniques

DTIC Science & Technology

2007-08-01

In this domain, queries typically show a deeply nested structure, which makes the semantic parsing task rather challenging , e.g.: What states border...only 80% of the GEOQUERY queries are semantically tractable, which shows that GEOQUERY is indeed a more challenging domain than ATIS. Note that none...a particularly challenging task, because of the inherent ambiguity of natural languages on both sides. It has inspired a large body of research. In
The paca that roared: Immediate cumulative semantic interference among newly acquired words.

PubMed

Oppenheim, Gary M

2018-08-01

With 40,000 words in the average vocabulary, how can speakers find the specific words that they want so quickly and easily? Cumulative semantic interference in language production provides a clue: when naming a large series of pictures, with a few mammals sprinkled about, naming each subsequent mammal becomes slower and more error-prone. Such interference mirrors predictions from an incremental learning algorithm applied to meaning-driven retrieval from an established vocabulary, suggesting retrieval benefits from a constant, implicit, re-optimization process (Oppenheim et al., 2010). But how quickly would a new mammal (e.g. paca) engage in this re-optimization? In this experiment, 18 participants studied 3 novel and 3 familiar exemplars from each of six semantic categories, and immediately performed a timed picture-naming task. Consistent with the learning model's predictions, naming latencies revealed immediate cumulative semantic interference in all directions: from new words to new words, from new words to old words, from old words to new words, and from old words to old words. Repeating the procedure several days later produced similar-magnitude effects, demonstrating that newly acquired words can be immediately semantically integrated, at least to the extent necessary to produce typical cumulative semantic interference. These findings extend the Dark Side model's scope to include novel word production, and are considered in terms of mechanisms for lexical selection. Copyright © 2018 Elsevier B.V. All rights reserved.
Kindling Fires: Examining the Potential for Cumulative Learning in a Journalism Curriculum

ERIC Educational Resources Information Center

Kilpert, Leigh; Shay, Suellen

2013-01-01

This study investigated context-dependency of learning as an indicator for students' potential to continue learning after graduation. We used Maton's theoretical concepts of "cumulative" and "segmented" learning, and "semantic gravity", to look for context-independent learning in students' assessments in a Journalism…
The semantic pathfinder: using an authoring metaphor for generic multimedia indexing.

PubMed

Snoek, Cees G M; Worring, Marcel; Geusebroek, Jan-Mark; Koelma, Dennis C; Seinstra, Frank J; Smeulders, Arnold W M

2006-10-01

This paper presents the semantic pathfinder architecture for generic indexing of multimedia archives. The semantic pathfinder extracts semantic concepts from video by exploring different paths through three consecutive analysis steps, which we derive from the observation that produced video is the result of an authoring-driven process. We exploit this authoring metaphor for machine-driven understanding. The pathfinder starts with the content analysis step. In this analysis step, we follow a data-driven approach of indexing semantics. The style analysis step is the second analysis step. Here, we tackle the indexing problem by viewing a video from the perspective of production. Finally, in the context analysis step, we view semantics in context. The virtue of the semantic pathfinder is its ability to learn the best path of analysis steps on a per-concept basis. To show the generality of this novel indexing approach, we develop detectors for a lexicon of 32 concepts and we evaluate the semantic pathfinder against the 2004 NIST TRECVID video retrieval benchmark, using a news archive of 64 hours. Top ranking performance in the semantic concept detection task indicates the merit of the semantic pathfinder for generic indexing of multimedia archives.
The Role of Semantics in Next-Generation Online Virtual World-Based Retail Store

NASA Astrophysics Data System (ADS)

Sharma, Geetika; Anantaram, C.; Ghosh, Hiranmay

Online virtual environments are increasingly becoming popular for entrepreneurship. While interactions are primarily between avatars, some interactions could occur through intelligent chatbots. Such interactions require connecting to backend business applications to obtain information, carry out real-world transactions etc. In this paper, we focus on integrating business application systems with virtual worlds. We discuss the probable features of a next-generation online virtual world-based retail store and the technologies involved in realizing the features of such a store. In particular, we examine the role of semantics in integrating popular virtual worlds with business applications to provide natural language based interactions.

Memory enhancement by a semantically unrelated emotional arousal source induced after learning.

PubMed

Nielson, Kristy A; Yee, Douglas; Erickson, Kirk I

2005-07-01

It has been well established that moderate physiological or emotional arousal modulates memory. However, there is some controversy about whether the source of arousal must be semantically related to the information to be remembered. To test this idea, 35 healthy young adult participants learned a list of common nouns and afterward viewed a semantically unrelated, neutral or emotionally arousing videotape. The tape was shown after learning to prevent arousal effects on encoding or attention, instead influencing memory consolidation. Heart rate increase was significantly greater in the arousal group, and negative affect was significantly less reported in the non-arousal group after the video. The arousal group remembered significantly more words than the non-arousal group at both 30 min and 24 h delays, despite comparable group memory performance prior to the arousal manipulation. These results demonstrate that emotional arousal, even from an unrelated source, is capable of modulating memory consolidation. Potential reasons for contradictory findings in some previous studies, such as the timing of "delayed" memory tests, are discussed.
Analyzing Hidden Semantics in Social Bookmarking of Open Educational Resources

NASA Astrophysics Data System (ADS)

Minguillón, Julià

Web 2.0 services such as social bookmarking allow users to manage and share the links they find interesting, adding their own tags for describing them. This is especially interesting in the field of open educational resources, as delicious is a simple way to bridge the institutional point of view (i.e. learning object repositories) with the individual one (i.e. personal collections), thus promoting the discovering and sharing of such resources by other users. In this paper we propose a methodology for analyzing such tags in order to discover hidden semantics (i.e. taxonomies and vocabularies) that can be used to improve descriptions of learning objects and make learning object repositories more visible and discoverable. We propose the use of a simple statistical analysis tool such as principal component analysis to discover which tags create clusters that can be semantically interpreted. We will compare the obtained results with a collection of resources related to open educational resources, in order to better understand the real needs of people searching for open educational resources.
Problems of teaching students to use the featured technologies in the area of semantic web

NASA Astrophysics Data System (ADS)

Klimov, V. V.; Chernyshov, A. A.; Balandina, A. I.; Kostkina, A. D.

2017-01-01

The following paper contains the description of up-to-date technologies in the area of web-services development, service-oriented architecture and the Semantic Web. The paper contains the analysis of the most popular and widespread technologies and methods in the semantic web area which are used in the developed educational course. In the paper, we also describe the problem of teaching students to use these technologies and specify conditions for the creation of the learning and development course. We also describe the main exercise for personal work and skills, which all the students learning this course have to gain. Moreover, in the paper we specify the problem with software which students are going to use while learning this course. In order to solve this problem, we introduce the developing system which will be used to support the laboratory works. For this moment this system supports only the fourth work execution, but our following plans contain the expansion of the system in order to support the leftover works.
Early onset marijuana use is associated with learning inefficiencies.

PubMed

Schuster, Randi Melissa; Hoeppner, Susanne S; Evins, A Eden; Gilman, Jodi M

2016-05-01

Verbal memory difficulties are the most widely reported and persistent cognitive deficit associated with early onset marijuana use. Yet, it is not known what memory stages are most impaired in those with early marijuana use. Forty-eight young adults, aged 18-25, who used marijuana at least once per week and 48 matched nonusing controls (CON) completed the California Verbal Learning Test, Second Edition (CVLT-II). Marijuana users were stratified by age of initial use: early onset users (EMJ), who started using marijuana at or before age 16 (n = 27), and late onset marijuana user group (LMJ), who started using marijuana after age 16 (n = 21). Outcome variables included trial immediate recall, total learning, clustering strategies (semantic clustering, serial clustering, ratio of semantic to serial clustering, and total number of strategies used), delayed recall, and percent retention. Learning improved with repetition, with no group effect on the learning slope. EMJ learned fewer words overall than LMJ or CON. There was no difference between LMJ and CON in total number of words learned. Reduced overall learning mediated the effect on reduced delayed recall among EMJ, but not CON or LMJ. Learning improved with greater use of semantic versus serial encoding, but this did not vary between groups. EMJ was not related to delayed recall after adjusting for encoding. Young adults reporting early onset marijuana use had learning weaknesses, which accounted for the association between early onset marijuana use and delayed recall. No amnestic effect of marijuana use was observed. (PsycINFO Database Record (c) 2016 APA, all rights reserved).
Early Onset Marijuana Use Is Associated with Learning Inefficiencies

PubMed Central

Schuster, Randi Melissa; Hoeppner, Susanne S.; Evins, A. Eden; Gilman, Jodi M.

2016-01-01

Objective Verbal memory difficulties are the most widely reported and persistent cognitive deficit associated with early-onset marijuana use. Yet, it is not known what memory stages are most impaired in those with early marijuana use. Method Forty-eight young adults, aged 18–25, who used marijuana at least once per week and 48 matched non-using controls (CON) completed the California Verbal Learning Test, Second Edition (CVLT-II). Marijuana users were stratified by age of initial use: ‘early onset’ users (EMJ), who started using marijuana at or before age 16 (n = 27), and ‘late onset’ marijuana user group (LMJ), who started using marijuana after age 16 (n = 21). Outcome variables included trial immediate recall, total learning, clustering strategies (semantic clustering, serial clustering, ratio of semantic to serial clustering, and total number of strategies used), delayed recall, and percent retention. Results Learning improved with repetition, with no group effect on the learning slope. EMJ learned fewer words overall than LMJ or CON. There was no difference between LMJ and CON in total number of words learned. Reduced overall learning mediated the effect on reduced delayed recall among EMJ, but not CON or LMJ. Learning improved with greater use of semantic versus serial encoding, but this did not vary between groups. EMJ was not related to delayed recall after adjusting for encoding. Conclusions Young adults reporting early onset marijuana use had learning weaknesses, which accounted for the association between early onset marijuana use and delayed recall. No amnestic effect of marijuana use was observed. PMID:26986749
Lesion Detection in CT Images Using Deep Learning Semantic Segmentation Technique

NASA Astrophysics Data System (ADS)

Kalinovsky, A.; Liauchuk, V.; Tarasau, A.

2017-05-01

In this paper, the problem of automatic detection of tuberculosis lesion on 3D lung CT images is considered as a benchmark for testing out algorithms based on a modern concept of Deep Learning. For training and testing of the algorithms a domestic dataset of 338 3D CT scans of tuberculosis patients with manually labelled lesions was used. The algorithms which are based on using Deep Convolutional Networks were implemented and applied in three different ways including slice-wise lesion detection in 2D images using semantic segmentation, slice-wise lesion detection in 2D images using sliding window technique as well as straightforward detection of lesions via semantic segmentation in whole 3D CT scans. The algorithms demonstrate superior performance compared to algorithms based on conventional image analysis methods.
Spatiotemporal Neural Dynamics of Word Understanding in 12- to 18-Month-Old-Infants

PubMed Central

Leonard, Matthew K.; Brown, Timothy T.; Hagler, Donald J.; Curran, Megan; Dale, Anders M.; Elman, Jeffrey L.; Halgren, Eric

2011-01-01

Learning words is central in human development. However, lacking clear evidence for how or where language is processed in the developing brain, it is unknown whether these processes are similar in infants and adults. Here, we use magnetoencephalography in combination with high-resolution structural magnetic resonance imaging to noninvasively estimate the spatiotemporal distribution of word-selective brain activity in 12- to 18-month-old infants. Infants watched pictures of common objects and listened to words that they understood. A subset of these infants also listened to familiar words compared with sensory control sounds. In both experiments, words evoked a characteristic event-related brain response peaking ∼400 ms after word onset, which localized to left frontotemporal cortices. In adults, this activity, termed the N400m, is associated with lexico-semantic encoding. Like adults, we find that the amplitude of the infant N400m is also modulated by semantic priming, being reduced to words preceded by a semantically related picture. These findings suggest that similar left frontotemporal areas are used for encoding lexico-semantic information throughout the life span, from the earliest stages of word learning. Furthermore, this ontogenetic consistency implies that the neurophysiological processes underlying the N400m may be important both for understanding already known words and for learning new words. PMID:21209121
SAS- Semantic Annotation Service for Geoscience resources on the web

NASA Astrophysics Data System (ADS)

Elag, M.; Kumar, P.; Marini, L.; Li, R.; Jiang, P.

2015-12-01

There is a growing need for increased integration across the data and model resources that are disseminated on the web to advance their reuse across different earth science applications. Meaningful reuse of resources requires semantic metadata to realize the semantic web vision for allowing pragmatic linkage and integration among resources. Semantic metadata associates standard metadata with resources to turn them into semantically-enabled resources on the web. However, the lack of a common standardized metadata framework as well as the uncoordinated use of metadata fields across different geo-information systems, has led to a situation in which standards and related Standard Names abound. To address this need, we have designed SAS to provide a bridge between the core ontologies required to annotate resources and information systems in order to enable queries and analysis over annotation from a single environment (web). SAS is one of the services that are provided by the Geosematnic framework, which is a decentralized semantic framework to support the integration between models and data and allow semantically heterogeneous to interact with minimum human intervention. Here we present the design of SAS and demonstrate its application for annotating data and models. First we describe how predicates and their attributes are extracted from standards and ingested in the knowledge-base of the Geosemantic framework. Then we illustrate the application of SAS in annotating data managed by SEAD and annotating simulation models that have web interface. SAS is a step in a broader approach to raise the quality of geoscience data and models that are published on the web and allow users to better search, access, and use of the existing resources based on standard vocabularies that are encoded and published using semantic technologies.
DESIGN FOR THINKING, A FIRST BOOK IN SEMANTICS.

ERIC Educational Resources Information Center

UPTON, ALBERT

THIS BOOK ABOUT THE FUNCTIONS OF LANGUAGE IN HUMAN LIFE EMPHASIZES LEARNING HOW TO CLASSIFY, DEFINE, AND ANALYZE. FOLLOWING AN EXPLANATION OF THE PHYSIOLOGICAL AND PSYCHOLOGICAL ROOTS OF LANGUAGE, CHAPTERS ON ANALYSIS, MEANING, SIGNS, AMBIGUITY, SEMANTIC GROWTH, AND METAPHOR LEAD TO A DESCRIPTION OF THE COMMUNICATIVE FUNCTION OF LANGUAGE,…
Grounding Collaborative Learning in Semantics-Based Critiquing

ERIC Educational Resources Information Center

Cheung, William K.; Mørch, Anders I.; Wong, Kelvin C.; Lee, Cynthia; Liu, Jiming; Lam, Mason H.

2007-01-01

In this article we investigate the use of latent semantic analysis (LSA), critiquing systems, and knowledge building to support computer-based teaching of English composition. We have built and tested an English composition critiquing system that makes use of LSA to analyze student essays and compute feedback by comparing their essays with…
Enhancing e-Learning Content by Using Semantic Web Technologies

ERIC Educational Resources Information Center

García-González, Herminio; Gayo, José Emilio Labra; del Puerto Paule-Ruiz, María

2017-01-01

We describe a new educational tool that relies on Semantic Web technologies to enhance lessons content. We conducted an experiment with 32 students whose results demonstrate better performance when exposed to our tool in comparison with a plain native tool. Consequently, this prototype opens new possibilities in lessons content enhancement.
A Large-Scale Analysis of Variance in Written Language

ERIC Educational Resources Information Center

Johns, Brendan T.; Jamieson, Randall K.

2018-01-01

The collection of very large text sources has revolutionized the study of natural language, leading to the development of several models of language learning and distributional semantics that extract sophisticated semantic representations of words based on the statistical redundancies contained within natural language (e.g., Griffiths, Steyvers,…
Development of Category-based Induction and Semantic Knowledge

ERIC Educational Resources Information Center

Fisher, Anna V.; Godwin, Karrie E.; Matlen, Bryan J.; Unger, Layla

2015-01-01

Category-based induction is a hallmark of mature cognition; however, little is known about its origins. This study evaluated the hypothesis that category-based induction is related to semantic development. Computational studies suggest that early on there is little differentiation among concepts, but learning and development lead to increased…
The Semantic Web in Education

ERIC Educational Resources Information Center

Ohler, Jason

2008-01-01

The semantic web or Web 3.0 makes information more meaningful to people by making it more understandable to machines. In this article, the author examines the implications of Web 3.0 for education. The author considers three areas of impact: knowledge construction, personal learning network maintenance, and personal educational administration.…
Virtual Field Sites: Losses and Gains in Authenticity with Semantic Technologies

ERIC Educational Resources Information Center

Litherland, Kate; Stott, Tim A.

2012-01-01

The authors investigate the potential of semantic web technologies to enhance "Virtual Fieldwork" resources and learning activities in the Geosciences. They consider the difficulties inherent in the concept of Virtual Fieldwork and how these might be reconciled with the desire to provide students with "authentic" tools for…
Shifting Interests: Changes in the Lexical Semantics of ED-MEDIA

ERIC Educational Resources Information Center

Wild, Fridolin; Valentine, Chris; Scott, Peter

2010-01-01

Large research networks naturally form complex communities with overlapping but not identical expertise. To map the distribution of professional competence in field of "technology-enhanced learning", the lexical semantics expressed in research articles published in a representative, large-scale conference (ED-MEDIA) can be investigated and changes…
Developing Online Learning Resources: Big Data, Social Networks, and Cloud Computing to Support Pervasive Knowledge

ERIC Educational Resources Information Center

Anshari, Muhammad; Alas, Yabit; Guan, Lim Sei

2016-01-01

Utilizing online learning resources (OLR) from multi channels in learning activities promise extended benefits from traditional based learning-centred to a collaborative based learning-centred that emphasises pervasive learning anywhere and anytime. While compiling big data, cloud computing, and semantic web into OLR offer a broader spectrum of…
Context-Adaptive Learning Designs by Using Semantic Web Services

ERIC Educational Resources Information Center

Dietze, Stefan; Gugliotta, Alessio; Domingue, John

2007-01-01

IMS Learning Design (IMS-LD) is a promising technology aimed at supporting learning processes. IMS-LD packages contain the learning process metadata as well as the learning resources. However, the allocation of resources--whether data or services--within the learning design is done manually at design-time on the basis of the subjective appraisals…
Semantic memory in developmental amnesia.

PubMed

Elward, Rachael L; Vargha-Khadem, Faraneh

2018-04-30

Patients with developmental amnesia resulting from bilateral hippocampal atrophy associated with neonatal hypoxia-ischaemia typically show relatively preserved semantic memory and factual knowledge about the natural world despite severe impairments in episodic memory. Understanding the neural and mnemonic processes that enable this context-free semantic knowledge to be acquired throughout development without the support of the contextualised episodic memory system is a serious challenge. This review describes the clinical presentation of patients with developmental amnesia, contrasts its features with those reported for adult-onset hippocampal amnesia, and analyses the effects of variables that influence the learning of new semantic information. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.
Semantic Knowledge for Famous Names in Mild Cognitive Impairment

PubMed Central

Seidenberg, Michael; Guidotti, Leslie; Nielson, Kristy A.; Woodard, John L.; Durgerian, Sally; Zhang, Qi; Gander, Amelia; Antuono, Piero; Rao, Stephen M.

2008-01-01

Person identification represents a unique category of semantic knowledge that is commonly impaired in Alzheimer's Disease (AD), but has received relatively little investigation in patients with Mild Cognitive Impairment (MCI). The current study examined the retrieval of semantic knowledge for famous names from three time epochs (recent, remote, and enduring) in two participant groups; 23 aMCI patients and 23 healthy elderly controls. The aMCI group was less accurate and produced less semantic knowledge than controls for famous names. Names from the enduring period were recognized faster than both recent and remote names in both groups, and remote names were recognized more quickly than recent names. Episodic memory performance was correlated with greater semantic knowledge particularly for recent names. We suggest that the anterograde memory deficits in the aMCI group interferes with learning of recent famous names and as a result produces difficulties with updating and integrating new semantic information with previously stored information. The implications of these findings for characterizing semantic memory deficits in MCI are discussed. PMID:19128524

Does task-irrelevant colour information create extraneous cognitive load? Evidence from a learning task.

PubMed

Miller, Paul; Hazan-Liran, Batel; Cohen, Danielle

2018-06-01

Previous studies have shown that task-irrelevant information impedes learning by creating extraneous cognitive load. But still open is whether such intrusion reflects a purely semantic phenomenon or whether it also stands for sheer perceptual interference. Using Cognitive Load Theory as a framework, this study aimed to answer this question by examining whether and how task-irrelevant colour information modifies extraneous cognitive load in relation to a new code-learning paradigm. For this purpose, university students were asked to learn, based on an example, associations between colour-related and colour-unrelated words and digits presented in black or in a mismatched ink colour. Evident costs in learning efficacy were found in learning the associations between words and digits for colour-related, but not for colour-unrelated, word stimuli. This suggests that interference by task-irrelevant information in learning stands for a mere semantic conflict. Implications of the findings for extraneous cognitive load on learning efficacy are discussed.
Episodic memory, semantic memory, and amnesia.

PubMed

Squire, L R; Zola, S M

1998-01-01

Episodic memory and semantic memory are two types of declarative memory. There have been two principal views about how this distinction might be reflected in the organization of memory functions in the brain. One view, that episodic memory and semantic memory are both dependent on the integrity of medial temporal lobe and midline diencephalic structures, predicts that amnesic patients with medial temporal lobe/diencephalic damage should be proportionately impaired in both episodic and semantic memory. An alternative view is that the capacity for semantic memory is spared, or partially spared, in amnesia relative to episodic memory ability. This article reviews two kinds of relevant data: 1) case studies where amnesia has occurred early in childhood, before much of an individual's semantic knowledge has been acquired, and 2) experimental studies with amnesic patients of fact and event learning, remembering and knowing, and remote memory. The data provide no compelling support for the view that episodic and semantic memory are affected differently in medial temporal lobe/diencephalic amnesia. However, episodic and semantic memory may be dissociable in those amnesic patients who additionally have severe frontal lobe damage.
Fast Brain Plasticity during Word Learning in Musically-Trained Children.

PubMed

Dittinger, Eva; Chobert, Julie; Ziegler, Johannes C; Besson, Mireille

2017-01-01

Children learn new words every day and this ability requires auditory perception, phoneme discrimination, attention, associative learning and semantic memory. Based on previous results showing that some of these functions are enhanced by music training, we investigated learning of novel words through picture-word associations in musically-trained and control children (8-12 year-old) to determine whether music training would positively influence word learning. Results showed that musically-trained children outperformed controls in a learning paradigm that included picture-sound matching and semantic associations. Moreover, the differences between unexpected and expected learned words, as reflected by the N200 and N400 effects, were larger in children with music training compared to controls after only 3 min of learning the meaning of novel words. In line with previous results in adults, these findings clearly demonstrate a correlation between music training and better word learning. It is argued that these benefits reflect both bottom-up and top-down influences. The present learning paradigm might provide a useful dynamic diagnostic tool to determine which perceptive and cognitive functions are impaired in children with learning difficulties.
Fast Brain Plasticity during Word Learning in Musically-Trained Children

PubMed Central

Dittinger, Eva; Chobert, Julie; Ziegler, Johannes C.; Besson, Mireille

2017-01-01

Children learn new words every day and this ability requires auditory perception, phoneme discrimination, attention, associative learning and semantic memory. Based on previous results showing that some of these functions are enhanced by music training, we investigated learning of novel words through picture-word associations in musically-trained and control children (8–12 year-old) to determine whether music training would positively influence word learning. Results showed that musically-trained children outperformed controls in a learning paradigm that included picture-sound matching and semantic associations. Moreover, the differences between unexpected and expected learned words, as reflected by the N200 and N400 effects, were larger in children with music training compared to controls after only 3 min of learning the meaning of novel words. In line with previous results in adults, these findings clearly demonstrate a correlation between music training and better word learning. It is argued that these benefits reflect both bottom-up and top-down influences. The present learning paradigm might provide a useful dynamic diagnostic tool to determine which perceptive and cognitive functions are impaired in children with learning difficulties. PMID:28553213
Semantic categorization: a comparison between deaf and hearing children.

PubMed

Ormel, Ellen A; Gijsel, Martine A R; Hermans, Daan; Bosman, Anna M T; Knoors, Harry; Verhoeven, Ludo

2010-01-01

Learning to read is a major obstacle for children who are deaf. The otherwise significant role of phonology is often limited as a result of hearing loss. However, semantic knowledge may facilitate reading comprehension. One important aspect of semantic knowledge concerns semantic categorization. In the present study, the quality of the semantic categorization of both deaf and hearing children was examined for written words and pictures at two categorization levels. The deaf children performed better at the picture condition compared to the written word condition, while the hearing children performed similarly at pictures and written words. The hearing children outperformed the deaf children, in particular for written words. In addition, the results of the deaf children for the written words correlated to their sign vocabulary and sign language comprehension. The increase in semantic categorization was limited across elementary school grade levels. Readers will be able to: (1) understand several semantic categorization differences between groups of deaf and hearing children; (2) describe factors that may affect the development of semantic categorization, in particular the relationship between sign language skills and semantic categorization for deaf children. Copyright 2010 Elsevier Inc. All rights reserved.
Research and Conceptualization of Ontologies in Intelligent Learning Systems

ERIC Educational Resources Information Center

Deliyska, Boryana; Manoilov, Peter

2010-01-01

The intelligent learning systems provide direct customized instruction to the learners without the intervention of human tutors on the basis of Semantic Web resources. Principal roles use ontologies as instruments for modeling learning processes, learners, learning disciplines and resources. This paper examines the variety, relationships, and…
Semantic Shot Classification in Sports Video

NASA Astrophysics Data System (ADS)

Duan, Ling-Yu; Xu, Min; Tian, Qi

2003-01-01

In this paper, we present a unified framework for semantic shot classification in sports videos. Unlike previous approaches, which focus on clustering by aggregating shots with similar low-level features, the proposed scheme makes use of domain knowledge of a specific sport to perform a top-down video shot classification, including identification of video shot classes for each sport, and supervised learning and classification of the given sports video with low-level and middle-level features extracted from the sports video. It is observed that for each sport we can predefine a small number of semantic shot classes, about 5~10, which covers 90~95% of sports broadcasting video. With the supervised learning method, we can map the low-level features to middle-level semantic video shot attributes such as dominant object motion (a player), camera motion patterns, and court shape, etc. On the basis of the appropriate fusion of those middle-level shot classes, we classify video shots into the predefined video shot classes, each of which has a clear semantic meaning. The proposed method has been tested over 4 types of sports videos: tennis, basketball, volleyball and soccer. Good classification accuracy of 85~95% has been achieved. With correctly classified sports video shots, further structural and temporal analysis, such as event detection, video skimming, table of content, etc, will be greatly facilitated.
Semantic attributes for people's appearance description: an appearance modality for video surveillance applications

NASA Astrophysics Data System (ADS)

Frikha, Mayssa; Fendri, Emna; Hammami, Mohamed

2017-09-01

Using semantic attributes such as gender, clothes, and accessories to describe people's appearance is an appealing modeling method for video surveillance applications. We proposed a midlevel appearance signature based on extracting a list of nameable semantic attributes describing the body in uncontrolled acquisition conditions. Conventional approaches extract the same set of low-level features to learn the semantic classifiers uniformly. Their critical limitation is the inability to capture the dominant visual characteristics for each trait separately. The proposed approach consists of extracting low-level features in an attribute-adaptive way by automatically selecting the most relevant features for each attribute separately. Furthermore, relying on a small training-dataset would easily lead to poor performance due to the large intraclass and interclass variations. We annotated large scale people images collected from different person reidentification benchmarks covering a large attribute sample and reflecting the challenges of uncontrolled acquisition conditions. These annotations were gathered into an appearance semantic attribute dataset that contains 3590 images annotated with 14 attributes. Various experiments prove that carefully designed features for learning the visual characteristics for an attribute provide an improvement of the correct classification accuracy and a reduction of both spatial and temporal complexities against state-of-the-art approaches.
Self-Regulated Workplace Learning: A Pedagogical Framework and Semantic Web-Based Environment

ERIC Educational Resources Information Center

Siadaty, Melody; Gasevic, Dragan; Jovanovic, Jelena; Pata, Kai; Milikic, Nikola; Holocher-Ertl, Teresa; Jeremic, Zoran; Ali, Liaqat; Giljanovic, Aleksandar; Hatala, Marek

2012-01-01

Self-regulated learning processes have a potential to enhance the motivation of knowledge workers to take part in learning and reflection about learning, and thus contribute to the resolution of an important research challenge in workplace learning. An equally important research challenge for the successful completion of each step of a…
Specifying the Mechanisms in a Levels-of-Processing Approach to Memory

ERIC Educational Resources Information Center

Klein, Kitty; Saltz, Eli

1976-01-01

Craik and Lockhart's (1972) levels-of-processing theory has spurred new interest in semantic processing as a factor in memory, particularly with regard to free recall following incidental learning. However, their formulation lacks a clear description of the operations and structures involved in semantic processing. This research outlines a…
Semantic Contamination and Mathematical Proof: Can a Non-Proof Prove?

ERIC Educational Resources Information Center

Mejia-Ramos, Juan Pablo; Inglis, Matthew

2011-01-01

The way words are used in natural language can influence how the same words are understood by students in formal educational contexts. Here we argue that this so-called semantic contamination effect plays a role in determining how students engage with mathematical proof, a fundamental aspect of learning mathematics. Analyses of responses to…
A Schema Theory Account of Some Cognitive Processes in Complex Learning. Technical Report No. 81.

ERIC Educational Resources Information Center

Munro, Allen; Rigney, Joseph W.

Procedural semantics models have diminished the distinction between data structures and procedures in computer simulations of human intelligence. This development has theoretical consequences for models of cognition. One type of procedural semantics model, called schema theory, is presented, and a variety of cognitive processes are explained in…
Reciprocal Effects of Self-Regulation, Semantic Knowledge, and Reading Comprehension in Early Elementary School

ERIC Educational Resources Information Center

Connor, Carol McDonald; Day, Stephanie L.; Phillips, Beth; Sparapani, Nicole; Ingebrand, Sarah W.; McLean, Leigh; Barrus, Angela; Kaschak, Michael P.

2016-01-01

Many assume that cognitive and linguistic processes, such as semantic knowledge (SK) and self-regulation (SR), subserve learned skills like reading. However, complex models of interacting and bootstrapping effects of SK, SR, instruction, and reading hypothesize reciprocal effects. Testing this "lattice" model with children (n = 852)…
An Investigation of Two Ways of Presenting Vocabulary

ERIC Educational Resources Information Center

Papathanasiou, Evagelia

2009-01-01

The use of semantic links or networks in L2 vocabulary acquisition has been a popular subject for numerous studies. On one hand, there is a strong theoretical background stating that presenting words in related fashion facilitates the learning of L2 vocabulary. On the other hand, research evidence indicates that semantically related vocabulary…
First- and Second-Order Metacognitive Judgments of Semantic Memory Reports: The Influence of Personality Traits and Cognitive Styles

ERIC Educational Resources Information Center

Buratti, Sandra; Allwood, Carl Martin; Kleitman, Sabina

2013-01-01

In learning contexts, people need to make realistic confidence judgments about their memory performance. The present study investigated whether second-order judgments of first-order confidence judgments could help people improve their confidence judgments of semantic memory information. Furthermore, we assessed whether different personality and…
Reasoning and Ontologies for Personalized E-Learning in the Semantic Web

ERIC Educational Resources Information Center

Henze, Nicola; Dolog, Peter; Nejdl, Wolfgang

2004-01-01

The challenge of the semantic web is the provision of distributed information with well-defined meaning, understandable for different parties. Particularly, applications should be able to provide individually optimized access to information by taking the individual needs and requirements of the users into account. In this paper we propose a…
Cue Conflicts in Context: Interplay between Morphosyntax and Discourse Context in Danish Preschoolers' Semantic Role Assignment

ERIC Educational Resources Information Center

Thomsen, Ditte Boeg; Poulsen, Mads

2015-01-01

When learning their first language, children develop strategies for assigning semantic roles to sentence structures, depending on morphosyntactic cues such as case and word order. Traditionally, comprehension experiments have presented transitive clauses in isolation, and cross-linguistically children have been found to misinterpret object-first…
Native-language N400 and P600 predict dissociable language-learning abilities in adults

PubMed Central

Qi, Zhenghan; Beach, Sara D.; Finn, Amy S.; Minas, Jennifer; Goetz, Calvin; Chan, Brian; Gabrieli, John D.E.

2018-01-01

Language learning aptitude during adulthood varies markedly across individuals. An individual’s native-language ability has been associated with success in learning a new language as an adult. However, little is known about how native-language processing affects learning success and what neural markers of native-language processing, if any, are related to success in learning. We therefore related variation in electrophysiology during native-language processing to success in learning a novel artificial language. Event-related potentials (ERPs) were recorded while native English speakers judged the acceptability of English sentences prior to learning an artificial language. There was a trend towards a double dissociation between native-language ERPs and their relationships to novel syntax and vocabulary learning. Individuals who exhibited a greater N400 effect when processing English semantics showed better future learning of the artificial language overall. The N400 effect was related to syntax learning via its specific relationship to vocabulary learning. In contrast, the P600 effect size when processing English syntax predicted future syntax learning but not vocabulary learning. These findings show that distinct neural signatures of native-language processing relate to dissociable abilities for learning novel semantic and syntactic information. PMID:27737775
Conjunctive patches subspace learning with side information for collaborative image retrieval.

PubMed

Zhang, Lining; Wang, Lipo; Lin, Weisi

2012-08-01

Content-Based Image Retrieval (CBIR) has attracted substantial attention during the past few years for its potential practical applications to image management. A variety of Relevance Feedback (RF) schemes have been designed to bridge the semantic gap between the low-level visual features and the high-level semantic concepts for an image retrieval task. Various Collaborative Image Retrieval (CIR) schemes aim to utilize the user historical feedback log data with similar and dissimilar pairwise constraints to improve the performance of a CBIR system. However, existing subspace learning approaches with explicit label information cannot be applied for a CIR task, although the subspace learning techniques play a key role in various computer vision tasks, e.g., face recognition and image classification. In this paper, we propose a novel subspace learning framework, i.e., Conjunctive Patches Subspace Learning (CPSL) with side information, for learning an effective semantic subspace by exploiting the user historical feedback log data for a CIR task. The CPSL can effectively integrate the discriminative information of labeled log images, the geometrical information of labeled log images and the weakly similar information of unlabeled images together to learn a reliable subspace. We formally formulate this problem into a constrained optimization problem and then present a new subspace learning technique to exploit the user historical feedback log data. Extensive experiments on both synthetic data sets and a real-world image database demonstrate the effectiveness of the proposed scheme in improving the performance of a CBIR system by exploiting the user historical feedback log data.
Predicting Robust Vocabulary Growth from Measures of Incremental Learning

ERIC Educational Resources Information Center

Frishkoff, Gwen A.; Perfetti, Charles A.; Collins-Thompson, Kevyn

2011-01-01

We report a study of incremental learning of new word meanings over multiple episodes. A new method called MESA (Markov Estimation of Semantic Association) tracked this learning through the automated assessment of learner-generated definitions. The multiple word learning episodes varied in the strength of contextual constraint provided by…

Statistical Learning as a Key to Cracking Chinese Orthographic Codes

ERIC Educational Resources Information Center

He, Xinjie; Tong, Xiuli

2017-01-01

This study examines statistical learning as a mechanism for Chinese orthographic learning among children in Grades 3-5. Using an artificial orthography, children were repeatedly exposed to positional, phonetic, and semantic regularities of radicals. Children showed statistical learning of all three regularities. Regularities' levels of consistency…
Proficiency and sentence constraint effects on second language word learning.

PubMed

Ma, Tengfei; Chen, Baoguo; Lu, Chunming; Dunlap, Susan

2015-07-01

This paper presents an experiment that investigated the effects of L2 proficiency and sentence constraint on semantic processing of unknown L2 words (pseudowords). All participants were Chinese native speakers who learned English as a second language. In the experiment, we used a whole sentence presentation paradigm with a delayed semantic relatedness judgment task. Both higher and lower-proficiency L2 learners could make use of the high-constraint sentence context to judge the meaning of novel pseudowords, and higher-proficiency L2 learners outperformed lower-proficiency L2 learners in all conditions. These results demonstrate that both L2 proficiency and sentence constraint affect subsequent word learning among second language learners. We extended L2 word learning into a sentence context, replicated the sentence constraint effects previously found among native speakers, and found proficiency effects in L2 word learning. Copyright © 2015 Elsevier B.V. All rights reserved.
Episodic, semantic and procedural memory in a case of amnesia at an early age.

PubMed

Ostergaard, A L

1987-01-01

The patient C.C. developed an amnesic syndrome at the age of 10 yr. Like adult amnesics, C.C. demonstrated impaired episodic memory for both verbal and visual materials although immediate memory span was spared. However, striking deficits were also observed on a wide variety of semantic memory tasks, including reading vocabulary and verbal fluency tests, semantic classification and lexical decision tasks and tests of verbal intelligence. On the other hand, C.C. showed normal learning and retention of two procedural tasks. It was argued that this evidence is inconsistent with the view that the amnesic syndrome represents a selective defect of episodic memory that leaves semantic memory relatively unaffected.
Evolution of costly explicit memory and cumulative culture.

PubMed

Nakamaru, Mayuko

2016-06-21

Humans can acquire new information and modify it (cumulative culture) based on their learning and memory abilities, especially explicit memory, through the processes of encoding, consolidation, storage, and retrieval. Explicit memory is categorized into semantic and episodic memories. Animals have semantic memory, while episodic memory is unique to humans and essential for innovation and the evolution of culture. As both episodic and semantic memory are needed for innovation, the evolution of explicit memory influences the evolution of culture. However, previous theoretical studies have shown that environmental fluctuations influence the evolution of imitation (social learning) and innovation (individual learning) and assume that memory is not an evolutionary trait. If individuals can store and retrieve acquired information properly, they can modify it and innovate new information. Therefore, being able to store and retrieve information is essential from the perspective of cultural evolution. However, if both storage and retrieval were too costly, forgetting and relearning would have an advantage over storing and retrieving acquired information. In this study, using mathematical analysis and individual-based simulations, we investigate whether cumulative culture can promote the coevolution of costly memory and social and individual learning, assuming that cumulative culture improves the fitness of each individual. The conclusions are: (1) without cumulative culture, a social learning cost is essential for the evolution of storage-retrieval. Costly storage-retrieval can evolve with individual learning but costly social learning does not evolve. When low-cost social learning evolves, the repetition of forgetting and learning is favored more than the evolution of costly storage-retrieval, even though a cultural trait improves the fitness. (2) When cumulative culture exists and improves fitness, storage-retrieval can evolve with social and/or individual learning, which is not influenced by the degree of the social learning cost. Whether individuals socially learn a low level of culture from observing a high or the low level of culture influences the evolution of memory and learning, especially individual learning. Copyright © 2016 Elsevier Ltd. All rights reserved.
Modeling loosely annotated images using both given and imagined annotations

NASA Astrophysics Data System (ADS)

Tang, Hong; Boujemaa, Nozha; Chen, Yunhao; Deng, Lei

2011-12-01

In this paper, we present an approach to learn latent semantic analysis models from loosely annotated images for automatic image annotation and indexing. The given annotation in training images is loose due to: 1. ambiguous correspondences between visual features and annotated keywords; 2. incomplete lists of annotated keywords. The second reason motivates us to enrich the incomplete annotation in a simple way before learning a topic model. In particular, some ``imagined'' keywords are poured into the incomplete annotation through measuring similarity between keywords in terms of their co-occurrence. Then, both given and imagined annotations are employed to learn probabilistic topic models for automatically annotating new images. We conduct experiments on two image databases (i.e., Corel and ESP) coupled with their loose annotations, and compare the proposed method with state-of-the-art discrete annotation methods. The proposed method improves word-driven probability latent semantic analysis (PLSA-words) up to a comparable performance with the best discrete annotation method, while a merit of PLSA-words is still kept, i.e., a wider semantic range.
Audiovisual semantic congruency during encoding enhances memory performance.

PubMed

Heikkilä, Jenni; Alho, Kimmo; Hyvönen, Heidi; Tiippana, Kaisa

2015-01-01

Studies of memory and learning have usually focused on a single sensory modality, although human perception is multisensory in nature. In the present study, we investigated the effects of audiovisual encoding on later unisensory recognition memory performance. The participants were to memorize auditory or visual stimuli (sounds, pictures, spoken words, or written words), each of which co-occurred with either a semantically congruent stimulus, incongruent stimulus, or a neutral (non-semantic noise) stimulus in the other modality during encoding. Subsequent memory performance was overall better when the stimulus to be memorized was initially accompanied by a semantically congruent stimulus in the other modality than when it was accompanied by a neutral stimulus. These results suggest that semantically congruent multisensory experiences enhance encoding of both nonverbal and verbal materials, resulting in an improvement in their later recognition memory.
Semantic Structure in Vocabulary Knowledge Interacts With Lexical and Sentence Processing in Infancy.

PubMed

Borovsky, Arielle; Ellis, Erica M; Evans, Julia L; Elman, Jeffrey L

2016-11-01

Although the size of a child's vocabulary associates with language-processing skills, little is understood regarding how this relation emerges. This investigation asks whether and how the structure of vocabulary knowledge affects language processing in English-learning 24-month-old children (N = 32; 18 F, 14 M). Parental vocabulary report was used to calculate semantic density in several early-acquired semantic categories. Performance on two language-processing tasks (lexical recognition and sentence processing) was compared as a function of semantic density. In both tasks, real-time comprehension was facilitated for higher density items, whereas lower density items experienced more interference. The findings indicate that language-processing skills develop heterogeneously and are influenced by the semantic network surrounding a known word. © 2016 The Authors. Child Development © 2016 Society for Research in Child Development, Inc.
Lifelong Learning Organisers: Requirements for Tools for Supporting Episodic and Semantic Learning

ERIC Educational Resources Information Center

Vavoula, Giasemi; Sharples, Mike

2009-01-01

We propose Lifelong Learning Organisers (LLOs) as tools to support the capturing, organisation and retrieval of personal learning experiences, resources and notes, over a range of learning topics, at different times and places. The paper discusses general requirements for the design of LLOs based on findings from a diary-based study of everyday…
Designing a Semantic Bliki System to Support Different Types of Knowledge and Adaptive Learning

ERIC Educational Resources Information Center

Huang, Shiu-Li; Yang, Chia-Wei

2009-01-01

Though blogs and wikis have been used to support knowledge management and e-learning, existing blogs and wikis cannot support different types of knowledge and adaptive learning. A case in point, types of knowledge vary greatly in category and viewpoints. Additionally, adaptive learning is crucial to improving one's learning performance. This study…
Building Interoperable Learning Objects Using Reduced Learning Object Metadata

ERIC Educational Resources Information Center

Saleh, Mostafa S.

2005-01-01

The new e-learning generation depends on Semantic Web technology to produce learning objects. As the production of these components is very costly, they should be produced and registered once, and reused and adapted in the same context or in other contexts as often as possible. To produce those components, developers should use learning standards…
The BioIntelligence Framework: a new computational platform for biomedical knowledge computing

PubMed Central

Farley, Toni; Kiefer, Jeff; Lee, Preston; Von Hoff, Daniel; Trent, Jeffrey M; Colbourn, Charles

2013-01-01

Breakthroughs in molecular profiling technologies are enabling a new data-intensive approach to biomedical research, with the potential to revolutionize how we study, manage, and treat complex diseases. The next great challenge for clinical applications of these innovations will be to create scalable computational solutions for intelligently linking complex biomedical patient data to clinically actionable knowledge. Traditional database management systems (DBMS) are not well suited to representing complex syntactic and semantic relationships in unstructured biomedical information, introducing barriers to realizing such solutions. We propose a scalable computational framework for addressing this need, which leverages a hypergraph-based data model and query language that may be better suited for representing complex multi-lateral, multi-scalar, and multi-dimensional relationships. We also discuss how this framework can be used to create rapid learning knowledge base systems to intelligently capture and relate complex patient data to biomedical knowledge in order to automate the recovery of clinically actionable information. PMID:22859646
Self-referential processing is distinct from semantic elaboration: evidence from long-term memory effects in a patient with amnesia and semantic impairments.

PubMed

Sui, Jie; Humphreys, Glyn W

2013-11-01

We report data demonstrating that self-referential encoding facilitates memory performance in the absence of effects of semantic elaboration in a severely amnesic patient also suffering semantic problems. In Part 1, the patient, GA, was trained to associate items with the self or a familiar other during the encoding phase of a memory task (self-ownership decisions in Experiment 1 and self-evaluation decisions in Experiment 2). Tests of memory showed a consistent self-reference advantage, relative to a condition where the reference was another person in both experiments. The pattern of the self-reference advantage was similar to that in healthy controls. In Part 2 we demonstrate that GA showed minimal effects of semantic elaboration on memory for items he semantically classified, compared with items subject to physical size decisions; in contrast, healthy controls demonstrated enhanced memory performance after semantic relative to physical encoding. The results indicate that self-referential encoding, not semantic elaboration, improves memory in amnesia. Self-referential processing may provide a unique scaffold to help improve learning in amnesic cases. Copyright © 2013 Elsevier Ltd. All rights reserved.
Classification with an edge: Improving semantic image segmentation with boundary detection

NASA Astrophysics Data System (ADS)

Marmanis, D.; Schindler, K.; Wegner, J. D.; Galliani, S.; Datcu, M.; Stilla, U.

2018-01-01

We present an end-to-end trainable deep convolutional neural network (DCNN) for semantic segmentation with built-in awareness of semantically meaningful boundaries. Semantic segmentation is a fundamental remote sensing task, and most state-of-the-art methods rely on DCNNs as their workhorse. A major reason for their success is that deep networks learn to accumulate contextual information over very large receptive fields. However, this success comes at a cost, since the associated loss of effective spatial resolution washes out high-frequency details and leads to blurry object boundaries. Here, we propose to counter this effect by combining semantic segmentation with semantically informed edge detection, thus making class boundaries explicit in the model. First, we construct a comparatively simple, memory-efficient model by adding boundary detection to the SEGNET encoder-decoder architecture. Second, we also include boundary detection in FCN-type models and set up a high-end classifier ensemble. We show that boundary detection significantly improves semantic segmentation with CNNs in an end-to-end training scheme. Our best model achieves >90% overall accuracy on the ISPRS Vaihingen benchmark.
Centre-surround inhibition is a general aspect of famous-person recognition: evidence from negative semantic priming from clearly visible primes.

PubMed

Stone, Anna

2012-05-01

A centre-surround attentional mechanism was proposed by Carr and Dagenbach (Journal of Experimental Psychology: Learning, Memory, and Cognition 16: 341-350, 1990) to account for their observations of negative semantic priming from hard-to-perceive primes. Their mechanism cannot account for the observation of negative semantic priming when primes are clearly visible. Three experiments (Ns = 30, 46, and 30) used a familiarity decision with names of famous people, preceded by a prime name with the same occupation as the target or with a different occupation. Negative semantic priming was observed at a 150- or 200-ms SOA, with positive priming at shorter (50-ms) and longer (1,000-ms) SOAs. In Experiment 3, we verified that the primes were easily recognisable in the priming task at an SOA that yielded negative semantic priming, which cannot be predicted by the original centre-surround mechanism. A modified version is proposed that explains transiently negative semantic priming by proposing that centre-surround inhibition is a normal, automatically invoked aspect of the semantic processing of visually presented famous names.
Photos That Increase Feelings of Learning Promote Positive Evaluations

ERIC Educational Resources Information Center

Cardwell, Brittany A.; Newman, Eryn J.; Garry, Maryanne; Mantonakis, Antonia; Beckett, Randi

2017-01-01

Research shows that when semantic context makes it feel easier for people to bring related thoughts and images to mind, people can misinterpret that feeling of ease as evidence that information is positive. But research also shows that semantic context does more than help people bring known concepts to mind--it also teaches people new concepts. In…
Influence of First Language Orthographic Experience on Second Language Decoding and Word Learning

ERIC Educational Resources Information Center

Hamada, Megumi; Koda, Keiko

2008-01-01

This study examined the influence of first language (L1) orthographic experiences on decoding and semantic information retention of new words in a second language (L2). Hypotheses were that congruity in L1 and L2 orthographic experiences determines L2 decoding efficiency, which, in turn, affects semantic information encoding and retention.…
Do Statistical Segmentation Abilities Predict Lexical-Phonological and Lexical-Semantic Abilities in Children with and without SLI?

ERIC Educational Resources Information Center

Mainela-Arnold, Elina; Evans, Julia L.

2014-01-01

This study tested the predictions of the procedural deficit hypothesis by investigating the relationship between sequential statistical learning and two aspects of lexical ability, lexical-phonological and lexical-semantic, in children with and without specific language impairment (SLI). Participants included forty children (ages 8;5-12;3), twenty…
Robust Deep Semantics for Language Understanding

DTIC Science & Technology

focus on five areas: deep learning, textual inferential relations, relation and event extraction by distant supervision , semantic parsing and...ontology expansion, and coreference resolution. As time went by, the program focus converged towards emphasizing technologies for knowledge base...natural logic methods for text understanding, improved mention coreference algorithms, and the further development of multilingual tools in CoreNLP.
Do Adults Show an Effect of Delayed First Language Acquisition When Calculating Scalar Implicatures?

ERIC Educational Resources Information Center

Davidson, Kathryn; Mayberry, Rachel I.

2015-01-01

Language acquisition involves learning not only grammatical rules and a lexicon but also what people are intending to convey with their utterances: the semantic/pragmatic component of language. In this article we separate the contributions of linguistic development and cognitive maturity to the acquisition of the semantic/pragmatic component of…
When Russians Learn English: How the Semantics of Causation May Change

ERIC Educational Resources Information Center

Wolff, Phillip; Ventura, Tatyana

2009-01-01

We examined how the semantics of causal expressions in Russian and English might differ and how these differences might lead to changes in the way second language learners understand causal expressions in their first language. According to the dynamics model of causation (Wolff, 2007), expressions of causation based on CAUSE verbs (make, force)…

Using a Semantic Diagram to Structure a Collaborative Problem Solving Process in the Classroom

ERIC Educational Resources Information Center

Cai, Huiying; Lin, Lin; Gu, Xiaoqing

2016-01-01

This study provides an in-depth look into the implementation process of visualization-based tools for structuring collaborative problem solving (CPS) in the classroom. A visualization-based learning platform--the semantic diagram for structuring CPS in a real classroom was designed and implemented. Metafora, the preliminary vehicle of the semantic…
Categorizing with Gender: Does Implicit Grammatical Gender Affect Semantic Processing in 24-Month-Old Toddlers?

ERIC Educational Resources Information Center

Bobb, Susan C.; Mani, Nivedita

2013-01-01

The current study investigated the interaction of implicit grammatical gender and semantic category knowledge during object identification. German-learning toddlers (24-month-olds) were presented with picture pairs and heard a noun (without a preceding article) labeling one of the pictures. Labels for target and distracter images either matched or…
Learning homophones in context: Easy cases are favored in the lexicon of natural languages.

PubMed

Dautriche, Isabelle; Fibla, Laia; Fievet, Anne-Caroline; Christophe, Anne

2018-08-01

Even though ambiguous words are common in languages, children find it hard to learn homophones, where a single label applies to several distinct meanings (e.g., Mazzocco, 1997). The present work addresses this apparent discrepancy between learning abilities and typological pattern, with respect to homophony in the lexicon. In a series of five experiments, 20-month-old French children easily learnt a pair of homophones if the two meanings associated with the phonological form belonged to different syntactic categories, or to different semantic categories. However, toddlers failed to learn homophones when the two meanings were distinguished only by different grammatical genders. In parallel, we analyzed the lexicon of four languages, Dutch, English, French and German, and observed that homophones are distributed non-arbitrarily in the lexicon, such that easily learnable homophones are more frequent than hard-to-learn ones: pairs of homophones are preferentially distributed across syntactic and semantic categories, but not across grammatical gender. We show that learning homophones is easier than previously thought, at least when the meanings of the same phonological form are made sufficiently distinct by their syntactic or semantic context. Following this, we propose that this learnability advantage translates into the overall structure of the lexicon, i.e., the kinds of homophones present in languages exhibit the properties that make them learnable by toddlers, thus allowing them to remain in languages. Copyright © 2018 Elsevier Inc. All rights reserved.
Maximal likelihood correspondence estimation for face recognition across pose.

PubMed

Li, Shaoxin; Liu, Xin; Chai, Xiujuan; Zhang, Haihong; Lao, Shihong; Shan, Shiguang

2014-10-01

Due to the misalignment of image features, the performance of many conventional face recognition methods degrades considerably in across pose scenario. To address this problem, many image matching-based methods are proposed to estimate semantic correspondence between faces in different poses. In this paper, we aim to solve two critical problems in previous image matching-based correspondence learning methods: 1) fail to fully exploit face specific structure information in correspondence estimation and 2) fail to learn personalized correspondence for each probe image. To this end, we first build a model, termed as morphable displacement field (MDF), to encode face specific structure information of semantic correspondence from a set of real samples of correspondences calculated from 3D face models. Then, we propose a maximal likelihood correspondence estimation (MLCE) method to learn personalized correspondence based on maximal likelihood frontal face assumption. After obtaining the semantic correspondence encoded in the learned displacement, we can synthesize virtual frontal images of the profile faces for subsequent recognition. Using linear discriminant analysis method with pixel-intensity features, state-of-the-art performance is achieved on three multipose benchmarks, i.e., CMU-PIE, FERET, and MultiPIE databases. Owe to the rational MDF regularization and the usage of novel maximal likelihood objective, the proposed MLCE method can reliably learn correspondence between faces in different poses even in complex wild environment, i.e., labeled face in the wild database.
Semantic Modelling for Learning Styles and Learning Material in an E-Learning Environment

ERIC Educational Resources Information Center

Alhasan, Khawla; Chen, Liming; Chen, Feng

2017-01-01

Various learners with various requirements have led to the raise of a crucial concern in the area of e-learning. A new technology for propagating learning to learners worldwide, has led to an evolution in the e-learning industry that takes into account all the requirements of the learning process. In spite of the wide growing, the e-learning…
Ontology Development and Evolution in the Accident Investigation Domain

NASA Technical Reports Server (NTRS)

Carvalho, Robert; Berrios, Dan; Williams, James

2004-01-01

InvestiigationOrganizer (IO) is a collaborative semantic web system designed to support the conduct of mishap investigations. IO provides a common repository for a wide range of mishap related information, allowing investigators to integrate evidence, causal models, and investigation results. IO has been used to support investigations ranging from a small property damage case to the loss of the Space Shuttle Columbia. Through IO'S use in these investigations, we have learned significant lessons? about the application of ontologies and semantic systems to solving real-world problems. This paper will describe the development of the ontology within IO, from the initial development, its growth in response to user requests during use in investigations, and the recent work that was done to control the results of that growth. This paper will also describe the lessons learned from this experience and how they may apply to the implementaton of future ontologies and semantic systems.
Semantic Framework of Internet of Things for Smart Cities: Case Studies.

PubMed

Zhang, Ningyu; Chen, Huajun; Chen, Xi; Chen, Jiaoyan

2016-09-14

In recent years, the advancement of sensor technology has led to the generation of heterogeneous Internet-of-Things (IoT) data by smart cities. Thus, the development and deployment of various aspects of IoT-based applications are necessary to mine the potential value of data to the benefit of people and their lives. However, the variety, volume, heterogeneity, and real-time nature of data obtained from smart cities pose considerable challenges. In this paper, we propose a semantic framework that integrates the IoT with machine learning for smart cities. The proposed framework retrieves and models urban data for certain kinds of IoT applications based on semantic and machine-learning technologies. Moreover, we propose two case studies: pollution detection from vehicles and traffic pattern detection. The experimental results show that our system is scalable and capable of accommodating a large number of urban regions with different types of IoT applications.
Semantic Framework of Internet of Things for Smart Cities: Case Studies

PubMed Central

Zhang, Ningyu; Chen, Huajun; Chen, Xi; Chen, Jiaoyan

2016-01-01

In recent years, the advancement of sensor technology has led to the generation of heterogeneous Internet-of-Things (IoT) data by smart cities. Thus, the development and deployment of various aspects of IoT-based applications are necessary to mine the potential value of data to the benefit of people and their lives. However, the variety, volume, heterogeneity, and real-time nature of data obtained from smart cities pose considerable challenges. In this paper, we propose a semantic framework that integrates the IoT with machine learning for smart cities. The proposed framework retrieves and models urban data for certain kinds of IoT applications based on semantic and machine-learning technologies. Moreover, we propose two case studies: pollution detection from vehicles and traffic pattern detection. The experimental results show that our system is scalable and capable of accommodating a large number of urban regions with different types of IoT applications. PMID:27649185
Semantic and visual memory codes in learning disabled readers.

PubMed

Swanson, H L

1984-02-01

Two experiments investigated whether learning disabled readers' impaired recall is due to multiple coding deficiencies. In Experiment 1, learning disabled and skilled readers viewed nonsense pictures without names or with either relevant or irrelevant names with respect to the distinctive characteristics of the picture. Both types of names improved recall of nondisabled readers, while learning disabled readers exhibited better recall for unnamed pictures. No significant difference in recall was found between name training (relevant, irrelevant) conditions within reading groups. In Experiment 2, both reading groups participated in recall training for complex visual forms labeled with unrelated words, hierarchically related words, or without labels. A subsequent reproduction transfer task showed a facilitation in performance in skilled readers due to labeling, with learning disabled readers exhibiting better reproduction for unnamed pictures. Measures of output organization (clustering) indicated that recall is related to the development of superordinate categories. The results suggest that learning disabled children's reading difficulties are due to an inability to activate a semantic representation that interconnects visual and verbal codes.
Semantic facilitation in bilingual first language acquisition.

PubMed

Bilson, Samuel; Yoshida, Hanako; Tran, Crystal D; Woods, Elizabeth A; Hills, Thomas T

2015-07-01

Bilingual first language learners face unique challenges that may influence the rate and order of early word learning relative to monolinguals. A comparison of the productive vocabularies of 435 children between the ages of 6 months and 7 years-181 of which were bilingual English learners-found that monolinguals learned both English words and all-language concepts faster than bilinguals. However, bilinguals showed an enhancement of an effect previously found in monolinguals-the preference for learning words with more associative cues. Though both monolinguals and bilinguals were best fit by a similar model of word learning, semantic network structure and growth indicated that the two groups were learning English words in a different order. Further, in comparison with a model of two-monolinguals-in-one-mind, bilinguals overproduced translational equivalents. Our results support an emergent account of bilingual first language acquisition, where learning a word in one language facilitates its acquisition in a second language. Copyright © 2015 Elsevier B.V. All rights reserved.
A Computational Unification of Scientific Law:. Spelling out a Universal Semantics for Physical Reality

NASA Astrophysics Data System (ADS)

Marcer, Peter J.; Rowlands, Peter

2013-09-01

The principal criteria Cn (n = 1 to 23) and grammatical production rules are set out of a universal computational rewrite language spelling out a semantic description of an emergent, self-organizing architecture for the cosmos. These language productions already predicate: (1) Einstein's conservation law of energy, momentum and mass and, subsequently, (2) with respect to gauge invariant relativistic space time (both Lorentz special & Einstein general); (3) Standard Model elementary particle physics; (4) the periodic table of the elements & chemical valence; and (5) the molecular biological basis of the DNA / RNA genetic code; so enabling the Cybernetic Machine specialist Groups Mission Statement premise;** (6) that natural semantic language thinking at the higher level of the self-organized emergent chemical molecular complexity of the human brain (only surpassed by that of the cosmos itself!) would be realized (7) by this same universal semantic language via (8) an architecture of a conscious human brain/mind and self which, it predicates consists of its neural / glia and microtubule substrates respectively, so as to endow it with; (9) the intelligent semantic capability to be able to specify, symbolize, spell out and understand the cosmos that conceived it; and (10) provide a quantum physical explanation of consciousness and of how (11) the dichotomy between first person subjectivity and third person objectivity or `hard problem' is resolved.
The Relationship between the Learning Strategies and Learning Styles in a Hypermedia Environment.

ERIC Educational Resources Information Center

Liu, Min; Reed, W. Michael

Different learning strategies that are used by field-independent (FI) and field-dependent (FD) people in a hypermedia-assisted instructional setting were studied with 63 international college students for whom English was a second language. The treatment was a semantic network-based hypermedia-assisted language-learning environment to help…
Utilizing Learners' Negative Ratings in Semantic Content-Based Recommender System for e-Learning Forum

ERIC Educational Resources Information Center

Albatayneh, Naji Ahmad; Ghauth, Khairil Imran; Chua, Fang-Fang

2018-01-01

Nowadays, most of e-learning systems embody online discussion forums as a medium for collaborative learning that supports knowledge sharing and information exchanging between learners. The exponential growth of the available shared information in e-learning online discussion forums has caused a difficulty for learners in discovering interesting…
Professional Music Training and Novel Word Learning: From Faster Semantic Encoding to Longer-lasting Word Representations.

PubMed

Dittinger, Eva; Barbaroux, Mylène; D'Imperio, Mariapaola; Jäncke, Lutz; Elmer, Stefan; Besson, Mireille

2016-10-01

On the basis of previous results showing that music training positively influences different aspects of speech perception and cognition, the aim of this series of experiments was to test the hypothesis that adult professional musicians would learn the meaning of novel words through picture-word associations more efficiently than controls without music training (i.e., fewer errors and faster RTs). We also expected musicians to show faster changes in brain electrical activity than controls, in particular regarding the N400 component that develops with word learning. In line with these hypotheses, musicians outperformed controls in the most difficult semantic task. Moreover, although a frontally distributed N400 component developed in both groups of participants after only a few minutes of novel word learning, in musicians this frontal distribution rapidly shifted to parietal scalp sites, as typically found for the N400 elicited by known words. Finally, musicians showed evidence for better long-term memory for novel words 5 months after the main experimental session. Results are discussed in terms of cascading effects from enhanced perception to memory as well as in terms of multifaceted improvements of cognitive processing due to music training. To our knowledge, this is the first report showing that music training influences semantic aspects of language processing in adults. These results open new perspectives for education in showing that early music training can facilitate later foreign language learning. Moreover, the design used in the present experiment can help to specify the stages of word learning that are impaired in children and adults with word learning difficulties.
Organizational Learning Strategies and Verbal Memory Deficits in Bipolar Disorder.

PubMed

Nitzburg, George C; Cuesta-Diaz, Armando; Ospina, Luz H; Russo, Manuela; Shanahan, Megan; Perez-Rodriguez, Mercedes; Larsen, Emmett; Mulaimovic, Sandra; Burdick, Katherine E

2017-04-01

Verbal memory (VM) impairment is prominent in bipolar disorder (BD) and is linked to functional outcomes. However, the intricacies of VM impairment have not yet been studied in a large sample of BD patients. Moreover, some have proposed VM deficits that may be mediated by organizational strategies, such as semantic or serial clustering. Thus, the exact nature of VM break-down in BD patients is not well understood, limiting remediation efforts. We investigated the intricacies of VM deficits in BD patients versus healthy controls (HCs) and examined whether verbal learning differences were mediated by use of clustering strategies. The California Verbal Learning Test (CVLT) was administered to 113 affectively stable BD patients and 106 HCs. We compared diagnostic groups on all CVLT indices and investigated whether group differences in verbal learning were mediated by clustering strategies. Although BD patients showed significantly poorer attention, learning, and memory, these indices were only mildly impaired. However, BD patients evidenced poorer use of effective learning strategies and lower recall consistency, with these indices falling in the moderately impaired range. Moreover, relative reliance on semantic clustering fully mediated the relationship between diagnostic category and verbal learning, while reliance on serial clustering partially mediated this relationship. VM deficits in affectively stable bipolar patients were widespread but were generally mildly impaired. However, patients displayed inadequate use of organizational strategies with clear separation from HCs on semantic and serial clustering. Remediation efforts may benefit from education about mnemonic devices or "chunking" techniques to attenuate VM deficits in BD. (JINS, 2017, 23, 358-366).
An ERP study of second language learning after childhood: effects of proficiency.

PubMed

Ojima, Shiro; Nakata, Hiroki; Kakigi, Ryusuke

2005-08-01

Whether there is an absolute critical period for acquiring language is a matter of continuous debate. One approach to address this issue is to compare the processes of second language (L2) learning after childhood and those of first language (L1) learning during childhood. To study the cortical process of postchildhood L2 learning, we compared event-related brain potentials recorded from two groups of adult Japanese speakers who attained either high or intermediate proficiency in English after childhood (J-High and J-Low), and adult native English speakers (ENG). Semantic anomalies embedded in English sentences evoked a clear N400 component in all three groups, with only the time course of the brain activation varying among the groups. Syntactic violations elicited a left-lateralized negativity similar to the left anterior negativity in ENG and J-High, but not in J-Low. In ENG, a P600 component was additionally found. These results suggest that semantic processing is robust from early on in L2 learning, whereas the development of syntactic processing is more dependent on proficiency as evidenced by the lack of the left-lateralized negativity in J-Low. Because early maturation and stability of semantic processing as opposed to syntactic processing are also a feature of L1 processing, postchildhood L2 learning may be governed by the same brain properties as those which govern childhood L1 learning. We argue that these processes are qualitatively similar in many respects, with only restricted domains of language processing being subject to absolute critical period effects.
Semantic memory for contextual regularities within and across scene categories: evidence from eye movements.

PubMed

Brockmole, James R; Le-Hoa Võ, Melissa

2010-10-01

When encountering familiar scenes, observers can use item-specific memory to facilitate the guidance of attention to objects appearing in known locations or configurations. Here, we investigated how memory for relational contingencies that emerge across different scenes can be exploited to guide attention. Participants searched for letter targets embedded in pictures of bedrooms. In a between-subjects manipulation, targets were either always on a bed pillow or randomly positioned. When targets were systematically located within scenes, search for targets became more efficient. Importantly, this learning transferred to bedrooms without pillows, ruling out learning that is based on perceptual contingencies. Learning also transferred to living room scenes, but it did not transfer to kitchen scenes, even though both scene types contained pillows. These results suggest that statistical regularities abstracted across a range of stimuli are governed by semantic expectations regarding the presence of target-predicting local landmarks. Moreover, explicit awareness of these contingencies led to a central tendency bias in recall memory for precise target positions that is similar to the spatial category effects observed in landmark memory. These results broaden the scope of conditions under which contextual cuing operates and demonstrate how semantic memory plays a causal and independent role in the learning of associations between objects in real-world scenes.
Orthographic influences on division of labor in learning to read Chinese and English: Insights from computational modeling

PubMed Central

Yang, Jianfeng; Shu, Hua; McCandliss, Bruce D.; Zevin, Jason D.

2013-01-01

Learning to read any language requires learning to map among print, sound and meaning. Writing systems differ in a number of factors that influence both the ease and rate with which reading skill can be acquired, as well as the eventual division of labor between phonological and semantic processes. Further, developmental reading disability manifests differently across writing systems, and may be related to different deficits in constitutive processes. Here we simulate some aspects of reading acquisition in Chinese and English using the same model architecture for both writing systems. The contribution of semantic and phonological processing to literacy acquisition in the two languages is simulated, including specific effects of phonological and semantic deficits. Further, we demonstrate that similar patterns of performance are observed when the same model is trained on both Chinese and English as an "early bilingual." The results are consistent with the view that reading skill is acquired by the application of statistical learning rules to mappings among print, sound and meaning, and that differences in the typical and disordered acquisition of reading skill between writing systems are driven by differences in the statistical patterns of the writing systems themselves, rather than differences in cognitive architecture of the learner. PMID:24587693
Comparative analysis of semantic localization accuracies between adult and pediatric DICOM CT images

NASA Astrophysics Data System (ADS)

Robertson, Duncan; Pathak, Sayan D.; Criminisi, Antonio; White, Steve; Haynor, David; Chen, Oliver; Siddiqui, Khan

2012-02-01

Existing literature describes a variety of techniques for semantic annotation of DICOM CT images, i.e. the automatic detection and localization of anatomical structures. Semantic annotation facilitates enhanced image navigation, linkage of DICOM image content and non-image clinical data, content-based image retrieval, and image registration. A key challenge for semantic annotation algorithms is inter-patient variability. However, while the algorithms described in published literature have been shown to cope adequately with the variability in test sets comprising adult CT scans, the problem presented by the even greater variability in pediatric anatomy has received very little attention. Most existing semantic annotation algorithms can only be extended to work on scans of both adult and pediatric patients by adapting parameters heuristically in light of patient size. In contrast, our approach, which uses random regression forests ('RRF'), learns an implicit model of scale variation automatically using training data. In consequence, anatomical structures can be localized accurately in both adult and pediatric CT studies without the need for parameter adaptation or additional information about patient scale. We show how the RRF algorithm is able to learn scale invariance from a combined training set containing a mixture of pediatric and adult scans. Resulting localization accuracy for both adult and pediatric data remains comparable with that obtained using RRFs trained and tested using only adult data.
Semantic and episodic memory in children with temporal lobe epilepsy: do they relate to literacy skills?

PubMed

Lah, Suncica; Smith, Mary Lou

2014-01-01

Children with temporal lobe epilepsy are at risk for deficits in new learning (episodic memory) and literacy skills. Semantic memory deficits and double dissociations between episodic and semantic memory have recently been found in this patient population. In the current study we investigate whether impairments of these 2 distinct memory systems relate to literacy skills. 57 children with unilateral temporal lobe epilepsy completed tests of verbal memory (episodic and semantic) and literacy skills (reading and spelling accuracy, and reading comprehension). For the entire group, semantic memory explained over 30% of variance in each of the literacy domains. Episodic memory explained a significant, but rather small proportion (< 10%) of variance in reading and spelling accuracy, but not in reading comprehension. Moreover, when children with opposite patterns of specific memory impairments (intact semantic/impaired episodic, intact episodic/impaired semantic) were compared, significant reductions in literacy skills were evident only in children with semantic memory impairments, but not in children with episodic memory impairments relative to the norms and to children with temporal lobe epilepsy who had intact memory. Our study provides the first evidence for differential relations between episodic and semantic memory impairments and literacy skills in children with temporal lobe epilepsy. As such, it highlights the urgent need to consider semantic memory deficits in management of children with temporal lobe epilepsy and undertake further research into the nature of reading difficulties of children with semantic memory impairments.

Competencies in Organizational E-Learning: Concepts and Tools

ERIC Educational Resources Information Center

Sicilia, Miguel-Angel, Ed.

2007-01-01

"Competencies in Organizational E-Learning: Concepts and Tools" provides a comprehensive view of the way competencies can be used to drive organizational e-learning, including the main conceptual elements, competency gap analysis, advanced related computing topics, the application of semantic Web technologies, and the integration of competencies…
Association of KIBRA and memory.

PubMed

Bates, Timothy C; Price, Jackie F; Harris, Sarah E; Marioni, Riccardo E; Fowkes, F Gerry R; Stewart, Marlene C; Murray, Gordon D; Whalley, Lawrence J; Starr, John M; Deary, Ian J

2009-07-24

We report on the association of KIBRA with memory in two samples of older individuals assessed on either memory for semantically unrelated word stimuli (Rey Auditory Verbal Learning Test, n=2091), or a measure of semantically related material (the WAIS Logical Memory Test of prose-passage recall, n=542). SNP rs17070145 was associated with delayed recall of semantically unrelated items, but not with immediate recall for these stimuli, nor with either immediate or delayed recall for semantically related material. The pattern of results suggests a role for the T-->C substitution in intron 9 of KIBRA in a component of episodic memory involved in long-term storage but independent of processes shared with immediate recall such as rehearsal involved in acquisition and rehearsal or processes.
Effect of semantic coherence on episodic memory processes in schizophrenia.

PubMed

Battal Merlet, Lâle; Morel, Shasha; Blanchet, Alain; Lockman, Hazlin; Kostova, Milena

2014-12-30

Schizophrenia is associated with severe episodic retrieval impairment. The aim of this study was to investigate the possibility that schizophrenia patients could improve their familiarity and/or recollection processes by manipulating the semantic coherence of to-be-learned stimuli and using deep encoding. Twelve schizophrenia patients and 12 healthy controls of comparable age, gender, and educational level undertook an associative recognition memory task. The stimuli consisted of pairs of words that were either related or unrelated to a given semantic category. The process dissociation procedure was used to calculate the estimates of familiarity and recollection processes. Both groups showed enhanced memory performances for semantically related words. However, in healthy controls, semantic relatedness led to enhanced recollection, while in schizophrenia patients, it induced enhanced familiarity. The familiarity estimates for related words were comparable in both groups, indicating that familiarity could be used as a compensatory mechanism in schizophrenia patients. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.
Semantic Encoding Enhances the Pictorial Superiority Effect in the Oldest-Old

PubMed Central

Cherry, Katie E.; Brown, Jennifer Silva; Walker, Erin Jackson; Smitherman, Emily A.; Boudreaux, Emily O.; Volaufova, Julia; Jazwinski, S. Michal

2011-01-01

We examined the effect of a semantic orienting task during encoding on free recall and recognition of simple line drawings and matching words in middle-aged (44 to 59 years), older (60 to 89 years), and oldest-old (90 + years) adults. Participants studied line drawings and matching words presented in blocked order. Half of the participants were given a semantic orienting task and the other half received standard intentional learning instructions. Results confirmed that the pictorial superiority effect was greater in magnitude following semantic encoding compared to the control condition. Analyses of clustering in free recall revealed that oldest-old adults’ encoding and retrieval strategies were generally similar to the two younger groups. Self-reported strategy use was less frequent among the oldest-old adults. These data strongly suggest that semantic elaboration is an effective compensatory mechanism underlying preserved episodic memory performance that persists well into the ninth decade of life. PMID:22053814
Enhancing acronym/abbreviation knowledge bases with semantic information.

PubMed

Torii, Manabu; Liu, Hongfang

2007-10-11

In the biomedical domain, a terminology knowledge base that associates acronyms/abbreviations (denoted as SFs) with the definitions (denoted as LFs) is highly needed. For the construction such terminology knowledge base, we investigate the feasibility to build a system automatically assigning semantic categories to LFs extracted from text. Given a collection of pairs (SF,LF) derived from text, we i) assess the coverage of LFs and pairs (SF,LF) in the UMLS and justify the need of a semantic category assignment system; and ii) automatically derive name phrases annotated with semantic category and construct a system using machine learning. Utilizing ADAM, an existing collection of (SF,LF) pairs extracted from MEDLINE, our system achieved an f-measure of 87% when assigning eight UMLS-based semantic groups to LFs. The system has been incorporated into a web interface which integrates SF knowledge from multiple SF knowledge bases. Web site: http://gauss.dbb.georgetown.edu/liblab/SFThesurus.
Personalized Learning Objects Recommendation Based on the Semantic-Aware Discovery and the Learner Preference Pattern

ERIC Educational Resources Information Center

Wang, Tzone I; Tsai, Kun Hua; Lee, Ming Che; Chiu, Ti Kai

2007-01-01

With vigorous development of the Internet, especially the web page interaction technology, distant E-learning has become more and more realistic and popular. Digital courses may consist of many learning units or learning objects and, currently, many learning objects are created according to SCORM standard. It can be seen that, in the near future,…
Native-language N400 and P600 predict dissociable language-learning abilities in adults.

PubMed

Qi, Zhenghan; Beach, Sara D; Finn, Amy S; Minas, Jennifer; Goetz, Calvin; Chan, Brian; Gabrieli, John D E

2017-04-01

Language learning aptitude during adulthood varies markedly across individuals. An individual's native-language ability has been associated with success in learning a new language as an adult. However, little is known about how native-language processing affects learning success and what neural markers of native-language processing, if any, are related to success in learning. We therefore related variation in electrophysiology during native-language processing to success in learning a novel artificial language. Event-related potentials (ERPs) were recorded while native English speakers judged the acceptability of English sentences prior to learning an artificial language. There was a trend towards a double dissociation between native-language ERPs and their relationships to novel syntax and vocabulary learning. Individuals who exhibited a greater N400 effect when processing English semantics showed better future learning of the artificial language overall. The N400 effect was related to syntax learning via its specific relationship to vocabulary learning. In contrast, the P600 effect size when processing English syntax predicted future syntax learning but not vocabulary learning. These findings show that distinct neural signatures of native-language processing relate to dissociable abilities for learning novel semantic and syntactic information. Copyright © 2016 Elsevier Ltd. All rights reserved.
Deep-learning derived features for lung nodule classification with limited datasets

NASA Astrophysics Data System (ADS)

Thammasorn, P.; Wu, W.; Pierce, L. A.; Pipavath, S. N.; Lampe, P. D.; Houghton, A. M.; Haynor, D. R.; Chaovalitwongse, W. A.; Kinahan, P. E.

2018-02-01

Only a few percent of indeterminate nodules found in lung CT images are cancer. However, enabling earlier diagnosis is important to avoid invasive procedures or long-time surveillance to those benign nodules. We are evaluating a classification framework using radiomics features derived with a machine learning approach from a small data set of indeterminate CT lung nodule images. We used a retrospective analysis of 194 cases with pulmonary nodules in the CT images with or without contrast enhancement from lung cancer screening clinics. The nodules were contoured by a radiologist and texture features of the lesion were calculated. In addition, sematic features describing shape were categorized. We also explored a Multiband network, a feature derivation path that uses a modified convolutional neural network (CNN) with a Triplet Network. This was trained to create discriminative feature representations useful for variable-sized nodule classification. The diagnostic accuracy was evaluated for multiple machine learning algorithms using texture, shape, and CNN features. In the CT contrast-enhanced group, the texture or semantic shape features yielded an overall diagnostic accuracy of 80%. Use of a standard deep learning network in the framework for feature derivation yielded features that substantially underperformed compared to texture and/or semantic features. However, the proposed Multiband approach of feature derivation produced results similar in diagnostic accuracy to the texture and semantic features. While the Multiband feature derivation approach did not outperform the texture and/or semantic features, its equivalent performance indicates promise for future improvements to increase diagnostic accuracy. Importantly, the Multiband approach adapts readily to different size lesions without interpolation, and performed well with relatively small amount of training data.
Learning Innovative Maternal Instinct: Activity Designing Semantic Factors of Alcohol Modification in Rural Communities of Thailand

ERIC Educational Resources Information Center

Yodmongkol, Pitipong; Jaimung, Thunyaporn; Chakpitak, Nopasit; Sureephong, Pradorn

2014-01-01

At present, Thailand is confronting a serious problem of alcohol drinking behavior which needs to be solved urgently. This research aimed to identify the semantic factors on alcohol drinking behavior and to use maternal instinct driving for housewives as village health volunteers in rural communities, Thailand. Two methods were implemented as the…
Supporting Semantic Annotation of Educational Content by Automatic Extraction of Hierarchical Domain Relationships

ERIC Educational Resources Information Center

Vrablecová, Petra; Šimko, Marián

2016-01-01

The domain model is an essential part of an adaptive learning system. For each educational course, it involves educational content and semantics, which is also viewed as a form of conceptual metadata about educational content. Due to the size of a domain model, manual domain model creation is a challenging and demanding task for teachers or…
A Learning Perspective on Individual Differences in Skilled Reading: Exploring and Exploiting Orthographic and Semantic Discrimination Cues

ERIC Educational Resources Information Center

Milin, Petar; Divjak, Dagmar; Baayen, R. Harald

2017-01-01

The goal of the present study is to understand the role orthographic and semantic information play in the behavior of skilled readers. Reading latencies from a self-paced sentence reading experiment in which Russian near-synonymous verbs were manipulated appear well-predicted by a combination of bottom-up sublexical letter triplets (trigraphs) and…
Semantic Web, Reusable Learning Objects, Personal Learning Networks in Health: Key Pieces for Digital Health Literacy.

PubMed

Konstantinidis, Stathis Th; Wharrad, Heather; Windle, Richard; Bamidis, Panagiotis D

2017-01-01

The knowledge existing in the World Wide Web is exponentially expanding, while continuous advancements in health sciences contribute to the creation of new knowledge. There are a lot of efforts trying to identify how the social connectivity can endorse patients' empowerment, while other studies look at the identification and the quality of online materials. However, emphasis has not been put on the big picture of connecting the existing resources with the patients "new habits" of learning through their own Personal Learning Networks. In this paper we propose a framework for empowering patients' digital health literacy adjusted to patients' currents needs by utilizing the contemporary way of learning through Personal Learning Networks, existing high quality learning resources and semantics technologies for interconnecting knowledge pieces. The framework based on the concept of knowledge maps for health as defined in this paper. Health Digital Literacy needs definitely further enhancement and the use of the proposed concept might lead to useful tools which enable use of understandable health trusted resources tailored to each person needs.
Primary School Teachers' Realization Levels of Self-Regulated Learning Practices and Sense of Efficacy

ERIC Educational Resources Information Center

Tanriseven, Isil

2013-01-01

The purpose of this study is to investigate primary school teachers' realization levels of self-regulated learning practices and sense of efficacy and the relationship between their realization levels of self-regulated learning practices and sense of efficacy. Survey research was conducted on 400 primary school teachers from 20 elementary schools…
Learning and Forgetting New Names and Objects in MCI and AD

ERIC Educational Resources Information Center

Gronholm-Nyman, Petra; Rinne, Juha O.; Laine, Matti

2010-01-01

We studied how subjects with mild cognitive impairment (MCI), early Alzheimer's disease (AD) and age-matched controls learned and maintained the names of unfamiliar objects that were trained with or without semantic support (object definitions). Naming performance, phonological cueing, incidental learning of the definitions and recognition of the…
English Orthographic Learning in Chinese-L1 Young EFL Beginners

ERIC Educational Resources Information Center

Cheng, Yu-Lin

2017-01-01

English orthographic learning, among Chinese-L1 children who were beginning to learn English as a foreign language, was documented when: (1) "only" visual memory was at their disposal, (2) visual memory and either "some" letter-sound knowledge or "some" semantic information was available, and (3) visual memory,…
An Approach to Folksonomy-Based Ontology Maintenance for Learning Environments

ERIC Educational Resources Information Center

Gasevic, D.; Zouaq, Amal; Torniai, Carlo; Jovanovic, J.; Hatala, Marek

2011-01-01

Recent research in learning technologies has demonstrated many promising contributions from the use of ontologies and semantic web technologies for the development of advanced learning environments. In spite of those benefits, ontology development and maintenance remain the key research challenges to be solved before ontology-enhanced learning…
Designing Distance Learning Tasks to Help Maximize Vocabulary Development

ERIC Educational Resources Information Center

Loucky, John Paul

2012-01-01

Task-based language learning using the benefits of online computer-assisted language learning (CALL) can be effective for rapid vocabulary expansion, especially when target vocabulary has been pre-arranged into bilingual categories under simpler, common Semantic Field Keywords. Results and satisfaction levels for both Chinese English majors and…
Expert Knowledge, Distinctiveness, and Levels of Processing in Language Learning

ERIC Educational Resources Information Center

Bird, Steve

2012-01-01

The foreign language vocabulary learning research literature often attributes strong mnemonic potency to the cognitive processing of meaning when learning words. Routinely cited as support for this idea are experiments by Craik and Tulving (C&T) demonstrating superior recognition and recall of studied words following semantic tasks ("deep"…
Implementation and Deployment of the IMS Learning Design Specification

ERIC Educational Resources Information Center

Paquette, Gilbert; Marino, Olga; De la Teja, Ileana; Lundgren-Cayrol, Karin; Lonard, Michel; Contamines, Julien

2005-01-01

Knowledge management in organizations, the learning objects paradigm, the advent of a new web generation, and the "Semantic Web" are major actual trends that reveal a potential for a renewed distance learning pedagogy. First and foremost is the use of educational modelling languages and instructional engineering methods to help decide…
Enabling Creative Learning Design through Semantic Technologies

ERIC Educational Resources Information Center

Charlton, Patricia; Magoulas, George; Laurillard, Diana

2012-01-01

The paper advocates an approach to learning design that considers it as creating digital artefacts that can be extended, modified and used for different purposes. This is realised through an "act becoming artefact" cycle, where users' actions in the authors' software environment, named Learning Designer, are automatically interpreted on…

A Methodology for Enhancing Mobile Learning through Content Semantics

ERIC Educational Resources Information Center

Dimitrios, Glaroudis; Athanasios, Manitsaris; Isabella, Kotini

2013-01-01

Mobile learning is becoming increasingly popular. Educational web sites can be used as supporting learning tools for students who wish to supplement their knowledge without restrictions of time and place. The continuously increasing demand for enhanced remote and mobile services, as well as the difficulty in easily incorporating current learning…
Jointly learning word embeddings using a corpus and a knowledge base

PubMed Central

Bollegala, Danushka; Maehara, Takanori; Kawarabayashi, Ken-ichi

2018-01-01

Methods for representing the meaning of words in vector spaces purely using the information distributed in text corpora have proved to be very valuable in various text mining and natural language processing (NLP) tasks. However, these methods still disregard the valuable semantic relational structure between words in co-occurring contexts. These beneficial semantic relational structures are contained in manually-created knowledge bases (KBs) such as ontologies and semantic lexicons, where the meanings of words are represented by defining the various relationships that exist among those words. We combine the knowledge in both a corpus and a KB to learn better word embeddings. Specifically, we propose a joint word representation learning method that uses the knowledge in the KBs, and simultaneously predicts the co-occurrences of two words in a corpus context. In particular, we use the corpus to define our objective function subject to the relational constrains derived from the KB. We further utilise the corpus co-occurrence statistics to propose two novel approaches, Nearest Neighbour Expansion (NNE) and Hedged Nearest Neighbour Expansion (HNE), that dynamically expand the KB and therefore derive more constraints that guide the optimisation process. Our experimental results over a wide-range of benchmark tasks demonstrate that the proposed method statistically significantly improves the accuracy of the word embeddings learnt. It outperforms a corpus-only baseline and reports an improvement of a number of previously proposed methods that incorporate corpora and KBs in both semantic similarity prediction and word analogy detection tasks. PMID:29529052
Phonological and Semantic Knowledge Are Causal Influences on Learning to Read Words in Chinese

ERIC Educational Resources Information Center

Zhou, Lulin; Duff, Fiona J.; Hulme, Charles

2015-01-01

We report a training study that assesses whether teaching the pronunciation and meaning of spoken words improves Chinese children's subsequent attempts to learn to read the words. Teaching the pronunciations of words helps children to learn to read those same words, and teaching the pronunciations and meanings improves learning still further.…
The Social Semantic Web in Intelligent Learning Environments: State of the Art and Future Challenges

ERIC Educational Resources Information Center

Jovanovic, Jelena; Gasevic, Dragan; Torniai, Carlo; Bateman, Scott; Hatala, Marek

2009-01-01

Today's technology-enhanced learning practices cater to students and teachers who use many different learning tools and environments and are used to a paradigm of interaction derived from open, ubiquitous, and socially oriented services. In this context, a crucial issue for education systems in general, and for Intelligent Learning Environments…
Knowledge acquisition is governed by striatal prediction errors.

PubMed

Pine, Alex; Sadeh, Noa; Ben-Yakov, Aya; Dudai, Yadin; Mendelsohn, Avi

2018-04-26

Discrepancies between expectations and outcomes, or prediction errors, are central to trial-and-error learning based on reward and punishment, and their neurobiological basis is well characterized. It is not known, however, whether the same principles apply to declarative memory systems, such as those supporting semantic learning. Here, we demonstrate with fMRI that the brain parametrically encodes the degree to which new factual information violates expectations based on prior knowledge and beliefs-most prominently in the ventral striatum, and cortical regions supporting declarative memory encoding. These semantic prediction errors determine the extent to which information is incorporated into long-term memory, such that learning is superior when incoming information counters strong incorrect recollections, thereby eliciting large prediction errors. Paradoxically, by the same account, strong accurate recollections are more amenable to being supplanted by misinformation, engendering false memories. These findings highlight a commonality in brain mechanisms and computational rules that govern declarative and nondeclarative learning, traditionally deemed dissociable.
Strong systematicity through sensorimotor conceptual grounding: an unsupervised, developmental approach to connectionist sentence processing

NASA Astrophysics Data System (ADS)

Jansen, Peter A.; Watter, Scott

2012-03-01

Connectionist language modelling typically has difficulty with syntactic systematicity, or the ability to generalise language learning to untrained sentences. This work develops an unsupervised connectionist model of infant grammar learning. Following the semantic boostrapping hypothesis, the network distils word category using a developmentally plausible infant-scale database of grounded sensorimotor conceptual representations, as well as a biologically plausible semantic co-occurrence activation function. The network then uses this knowledge to acquire an early benchmark clausal grammar using correlational learning, and further acquires separate conceptual and grammatical category representations. The network displays strongly systematic behaviour indicative of the general acquisition of the combinatorial systematicity present in the grounded infant-scale language stream, outperforms previous contemporary models that contain primarily noun and verb word categories, and successfully generalises broadly to novel untrained sensorimotor grounded sentences composed of unfamiliar nouns and verbs. Limitations as well as implications to later grammar learning are discussed.
Interactions between statistical and semantic information in infant language development

PubMed Central

Lany, Jill; Saffran, Jenny R.

2013-01-01

Infants can use statistical regularities to form rudimentary word categories (e.g. noun, verb), and to learn the meanings common to words from those categories. Using an artificial language methodology, we probed the mechanisms by which two types of statistical cues (distributional and phonological regularities) affect word learning. Because linking distributional cues vs. phonological information to semantics make different computational demands on learners, we also tested whether their use is related to language proficiency. We found that 22-month-old infants with smaller vocabularies generalized using phonological cues; however, infants with larger vocabularies showed the opposite pattern of results, generalizing based on distributional cues. These findings suggest that both phonological and distributional cues marking word categories promote early word learning. Moreover, while correlations between these cues are important to forming word categories, we found infants’ weighting of these cues in subsequent word-learning tasks changes over the course of early language development. PMID:21884336
Realizing Outdoor Independent Learning with a Butterfly-Watching Mobile Learning System

ERIC Educational Resources Information Center

Chen, Yuh-Shyan; Kao, Tai-Chien; Sheu, Jang-Ping

2005-01-01

In this article, we describe the development of a mobile butterfly-watching learning (BWL) system to realize outdoor independent learning for mobile learners. The mobile butterfly-watching learning system was designed in a wireless mobile ad-hoc learning environment. This is first result to provide a cognitive tool with supporting the independent…
Differentiation of perceptual and semantic subsequent memory effects using an orthographic paradigm.

PubMed

Kuo, Michael C C; Liu, Karen P Y; Ting, Kin Hung; Chan, Chetwyn C H

2012-11-27

This study aimed to differentiate perceptual and semantic encoding processes using subsequent memory effects (SMEs) elicited by the recognition of orthographs of single Chinese characters. Participants studied a series of Chinese characters perceptually (by inspecting orthographic components) or semantically (by determining the object making sounds), and then made studied or unstudied judgments during the recognition phase. Recognition performance in terms of d-prime measure in the semantic condition was higher, though not significant, than that of the perceptual condition. The between perceptual-semantic condition differences in SMEs at P550 and late positive component latencies (700-1000ms) were not significant in the frontal area. An additional analysis identified larger SME in the semantic condition during 600-1000ms in the frontal pole regions. These results indicate that coordination and incorporation of orthographic information into mental representation is essential to both task conditions. The differentiation was also revealed in earlier SMEs (perceptual>semantic) at N3 (240-360ms) latency, which is a novel finding. The left-distributed N3 was interpreted as more efficient processing of meaning with semantically learned characters. Frontal pole SMEs indicated strategic processing by executive functions, which would further enhance memory. Copyright © 2012 Elsevier B.V. All rights reserved.
Knowledge representation and management: benefits and challenges of the semantic web for the fields of KRM and NLP.

PubMed

Rassinoux, A-M

2011-01-01

To summarize excellent current research in the field of knowledge representation and management (KRM). A synopsis of the articles selected for the IMIA Yearbook 2011 is provided and an attempt to highlight the current trends in the field is sketched. This last decade, with the extension of the text-based web towards a semantic-structured web, NLP techniques have experienced a renewed interest in knowledge extraction. This trend is corroborated through the five papers selected for the KRM section of the Yearbook 2011. They all depict outstanding studies that exploit NLP technologies whenever possible in order to accurately extract meaningful information from various biomedical textual sources. Bringing semantic structure to the meaningful content of textual web pages affords the user with cooperative sharing and intelligent finding of electronic data. As exemplified by the best paper selection, more and more advanced biomedical applications aim at exploiting the meaningful richness of free-text documents in order to generate semantic metadata and recently to learn and populate domain ontologies. These later are becoming a key piece as they allow portraying the semantics of the Semantic Web content. Maintaining their consistency with documents and semantic annotations that refer to them is a crucial challenge of the Semantic Web for the coming years.
Semantic and phonological schema influence spoken word learning and overnight consolidation.

PubMed

Havas, Viktória; Taylor, Jsh; Vaquero, Lucía; de Diego-Balaguer, Ruth; Rodríguez-Fornells, Antoni; Davis, Matthew H

2018-06-01

We studied the initial acquisition and overnight consolidation of new spoken words that resemble words in the native language (L1) or in an unfamiliar, non-native language (L2). Spanish-speaking participants learned the spoken forms of novel words in their native language (Spanish) or in a different language (Hungarian), which were paired with pictures of familiar or unfamiliar objects, or no picture. We thereby assessed, in a factorial way, the impact of existing knowledge (schema) on word learning by manipulating both semantic (familiar vs unfamiliar objects) and phonological (L1- vs L2-like novel words) familiarity. Participants were trained and tested with a 12-hr intervening period that included overnight sleep or daytime awake. Our results showed (1) benefits of sleep to recognition memory that were greater for words with L2-like phonology and (2) that learned associations with familiar but not unfamiliar pictures enhanced recognition memory for novel words. Implications for complementary systems accounts of word learning are discussed.
Naming and verbal learning in adults with Alzheimer's disease, mild cognitive impairment and in healthy aging, with low educational levels.

PubMed

Hübner, Lilian Cristine; Loureiro, Fernanda; Tessaro, Bruna; Siqueira, Ellen Cristina Gerner; Jerônimo, Gislaine Machado; Gomes, Irênio; Schilling, Lucas Porcello

2018-02-01

Language assessment seems to be an effective tool to differentiate healthy and cognitively impaired aging groups. This article discusses the impact of educational level on a naming task, on a verbal learning with semantic cues task and on the MMSE in healthy aging adults at three educational levels (very low, low and high) as well as comparing two clinical groups of very low (0-3 years) and low education (4-7 years) patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI) with healthy controls. The participants comprised 101 healthy controls, 17 patients with MCI and 19 with AD. Comparisons between the healthy groups showed an education effect on the MMSE, but not on naming and verbal learning. However, the clinical groups were differentiated in both the naming and verbal learning assessment. The results support the assumption that the verbal learning with semantic cues task is a valid tool to diagnose MCI and AD patients, with no influence from education.
A New Semantic List Learning Task to Probe Functioning of the Papez Circuit

PubMed Central

Schallmo, Michael-Paul; Kassel, Michelle T.; Weisenbach, Sara L.; Walker, Sara J.; Guidotti-Breting, Leslie M.; Rao, Julia A.; Hazlett, Kathleen E.; Considine, Ciaran M.; Sethi, Gurpriya; Vats, Naalti; Pecina, Marta; Welsh, Robert C.; Starkman, Monica N.; Giordani, Bruno; Langenecker, Scott A.

2016-01-01

Introduction List learning tasks are powerful clinical tools for studying memory, yet have been relatively underutilized within the functional imaging literature. This limits understanding of regions such as the Papez circuit which support memory performance in healthy, non-demented adults. Method The current study characterized list learning performance in 40 adults who completed a Semantic List Learning Task (SLLT) with a Brown-Peterson manipulation during functional MRI (fMRI). Cued recall with semantic cues, and recognition memory were assessed after imaging. Internal reliability and convergent and discriminant validity were evaluated. Results Subjects averaged 38% accuracy in recall (62% for recognition), with primacy but no recency effects observed. Validity and reliability were demonstrated by showing that the SLLT was correlated with the California Verbal Learning test (CVLT), but not with executive functioning tests, and high intraclass correlation coefficient across lists for recall (.91). fMRI measurements during Encoding (vs. Silent Rehearsal) revealed significant activation in bilateral hippocampus, parahippocampus, and bilateral anterior and posterior cingulate cortex. Post-hoc analyses showed increased activation in anterior and middle hippocampus, subgenual cingulate, and mammillary bodies specific to Encoding. In addition, increasing age was positively associated with increased activation in a diffuse network, particularly frontal cortex and specific Papez regions for correctly recalled words. Gender differences were specific to left inferior and superior frontal cortex. Conclusions This is a clinically relevant list learning task that can be used in studies of groups for which the Papez circuit is damaged or disrupted, in mixed or crossover studies at imaging and clinical sites. PMID:26313512
Long-term interference at the semantic level: Evidence from blocked-cyclic picture matching.

PubMed

Wei, Tao; Schnur, Tatiana T

2016-01-01

Processing semantically related stimuli creates interference across various domains of cognition, including language and memory. In this study, we identify the locus and mechanism of interference when retrieving meanings associated with words and pictures. Subjects matched a probe stimulus (e.g., cat) to its associated target picture (e.g., yarn) from an array of unrelated pictures. Across trials, probes were either semantically related or unrelated. To test the locus of interference, we presented probes as either words or pictures. If semantic interference occurs at the stage common to both tasks, that is, access to semantic representations, then interference should occur in both probe presentation modalities. Results showed clear semantic interference effects independent of presentation modality and lexical frequency, confirming a semantic locus of interference in comprehension. To test the mechanism of interference, we repeated trials across 4 presentation cycles and manipulated the number of unrelated intervening trials (zero vs. two). We found that semantic interference was additive across cycles and survived 2 intervening trials, demonstrating interference to be long-lasting as opposed to short-lived. However, interference was smaller with zero versus 2 intervening trials, which we interpret to suggest that short-lived facilitation counteracted the long-lived interference. We propose that retrieving meanings associated with words/pictures from the same semantic category yields both interference due to long-lasting changes in connection strength between semantic representations (i.e., incremental learning) and facilitation caused by short-lived residual activation. (PsycINFO Database Record (c) 2016 APA, all rights reserved).
Semantic-based surveillance video retrieval.

PubMed

Hu, Weiming; Xie, Dan; Fu, Zhouyu; Zeng, Wenrong; Maybank, Steve

2007-04-01

Visual surveillance produces large amounts of video data. Effective indexing and retrieval from surveillance video databases are very important. Although there are many ways to represent the content of video clips in current video retrieval algorithms, there still exists a semantic gap between users and retrieval systems. Visual surveillance systems supply a platform for investigating semantic-based video retrieval. In this paper, a semantic-based video retrieval framework for visual surveillance is proposed. A cluster-based tracking algorithm is developed to acquire motion trajectories. The trajectories are then clustered hierarchically using the spatial and temporal information, to learn activity models. A hierarchical structure of semantic indexing and retrieval of object activities, where each individual activity automatically inherits all the semantic descriptions of the activity model to which it belongs, is proposed for accessing video clips and individual objects at the semantic level. The proposed retrieval framework supports various queries including queries by keywords, multiple object queries, and queries by sketch. For multiple object queries, succession and simultaneity restrictions, together with depth and breadth first orders, are considered. For sketch-based queries, a method for matching trajectories drawn by users to spatial trajectories is proposed. The effectiveness and efficiency of our framework are tested in a crowded traffic scene.
Acquiring and processing verb argument structure: distributional learning in a miniature language.

PubMed

Wonnacott, Elizabeth; Newport, Elissa L; Tanenhaus, Michael K

2008-05-01

Adult knowledge of a language involves correctly balancing lexically-based and more language-general patterns. For example, verb argument structures may sometimes readily generalize to new verbs, yet with particular verbs may resist generalization. From the perspective of acquisition, this creates significant learnability problems, with some researchers claiming a crucial role for verb semantics in the determination of when generalization may and may not occur. Similarly, there has been debate regarding how verb-specific and more generalized constraints interact in sentence processing and on the role of semantics in this process. The current work explores these issues using artificial language learning. In three experiments using languages without semantic cues to verb distribution, we demonstrate that learners can acquire both verb-specific and verb-general patterns, based on distributional information in the linguistic input regarding each of the verbs as well as across the language as a whole. As with natural languages, these factors are shown to affect production, judgments and real-time processing. We demonstrate that learners apply a rational procedure in determining their usage of these different input statistics and conclude by suggesting that a Bayesian perspective on statistical learning may be an appropriate framework for capturing our findings.
A Semantic Web-Based Authoring Tool to Facilitate the Planning of Collaborative Learning Scenarios Compliant with Learning Theories

ERIC Educational Resources Information Center

Isotani, Seiji; Mizoguchi, Riichiro; Isotani, Sadao; Capeli, Olimpio M.; Isotani, Naoko; de Albuquerque, Antonio R. P. L.; Bittencourt, Ig. I.; Jaques, Patricia

2013-01-01

When the goal of group activities is to support long-term learning, the task of designing well-thought-out collaborative learning (CL) scenarios is an important key to success. To help students adequately acquire and develop their knowledge and skills, a teacher can plan a scenario that increases the probability for learning to occur. Such a…
Utility of behavioral versus cognitive measures in differentiating between subtypes of frontotemporal lobar degeneration and Alzheimer's disease.

PubMed

Heidler-Gary, Jennifer; Gottesman, Rebecca; Newhart, Melissa; Chang, Shannon; Ken, Lynda; Hillis, Argye E

2007-01-01

We hypothesized that a modified version of the Frontal Behavioral Inventory (FBI-mod), along with a few cognitive tests, would be clinically useful in distinguishing between clinically defined Alzheimer's disease (AD) and subtypes of frontotemporal lobar degeneration (FTLD): frontotemporal dementia (dysexecutive type), progressive nonfluent aphasia, and semantic dementia. We studied 80 patients who were diagnosed with AD (n = 30) or FTLD (n = 50), on the basis of a comprehensive neuropsychological battery, imaging, neurological examination, and history. We found significant between-group differences on the FBI-mod, two subtests of the Rey Auditory Verbal Learning Test (verbal learning and delayed recall), and the Trail Making Test Part B (one measure of 'executive functioning'). AD was characterized by relatively severe impairment in verbal learning, delayed recall, and executive functioning, with relatively normal scores on the FBI-mod. Frontotemporal dementia was characterized by relatively severe impairment on the FBI-mod and executive functioning in the absence of severe impairment in verbal learning and recall. Progressive nonfluent aphasia was characterized by severe impairment in executive functioning with relatively normal scores on verbal learning and recall and FBI-mod. Finally, semantic dementia was characterized by relatively severe deficits in delayed recall, but relatively normal performance on new learning, executive functioning, and on FBI-mod. Discriminant function analysis confirmed that the FBI-mod, in conjunction with the Rey Auditory Verbal Learning Test, and the Trail Making Test Part B categorized the majority of patients as subtypes of FTLD or AD in the same way as a full neuropsychological battery, neurological examination, complete history, and imaging. These tests may be useful for efficient clinical diagnosis, although progressive nonfluent aphasia and semantic dementia are likely to be best distinguished by language tests not included in standard neuropsychological test batteries.
Incidental Learning and Recall in Children.

ERIC Educational Resources Information Center

Fox, Robert A.; And Others

Incidental learning research with mentally retarded children has produced findings inconsistent with those reported for the intellectually normal population. This study was designed to further investigate the efficacy of incidental semantic classification instructions relative to taxonomic classification instructions or superficial color…
The Architecture of Intuition: Fluency and Affect Determine Intuitive Judgments of Semantic and Visual Coherence and Judgments of Grammaticality in Artificial Grammar Learning

ERIC Educational Resources Information Center

Topolinski, Sascha; Strack, Fritz

2009-01-01

People can intuitively detect whether a word triad has a common remote associate (coherent) or does not have one (incoherent) before and independently of actually retrieving the common associate. The authors argue that semantic coherence increases the processing fluency for coherent triads and that this increased fluency triggers a brief and…

The Dark Side of Incremental Learning: A Model of Cumulative Semantic Interference during Lexical Access in Speech Production

ERIC Educational Resources Information Center

Oppenheim, Gary M.; Dell, Gary S.; Schwartz, Myrna F.

2010-01-01

Naming a picture of a dog primes the subsequent naming of a picture of a dog (repetition priming) and interferes with the subsequent naming of a picture of a cat (semantic interference). Behavioral studies suggest that these effects derive from persistent changes in the way that words are activated and selected for production, and some have…
A Mathematical Analysis of Semantic Maps, with Theoretical and Applied Implications for Blended Learning Software

ERIC Educational Resources Information Center

Tang, Michael; David, Hyerle; Byrne, Roxanne; Tran, John

2012-01-01

This paper is a mathematical (Boolean) analysis a set of cognitive maps called Thinking Maps[R], based on Albert Upton's semantic principles developed in his seminal works, Design for Thinking (1961) and Creative Analysis (1961). Albert Upton can be seen as a brilliant thinker who was before his time or after his time depending on the future of…
Visual Field Differences in Visual Word Recognition Can Emerge Purely from Perceptual Learning: Evidence from Modeling Chinese Character Pronunciation

ERIC Educational Resources Information Center

Hsiao, Janet Hui-wen

2011-01-01

In Chinese orthography, a dominant character structure exists in which a semantic radical appears on the left and a phonetic radical on the right (SP characters); a minority opposite arrangement also exists (PS characters). As the number of phonetic radical types is much greater than semantic radical types, in SP characters the information is…
Developmental Differences in the Effects of Phonological, Lexical and Semantic Variables on Word Learning by Infants

ERIC Educational Resources Information Center

Storkel, Holly L.

2009-01-01

The influence of phonological (i.e. individual sounds), lexical (i.e. whole-word forms) and semantic (i.e. meaning) characteristics on the words known by infants age 1;4 to 2;6 was examined, using an existing database (Dale & Fenson, 1996). For each noun, word frequency, two phonological (i.e. positional segment average, biphone average), two…
Dissociating the effects of semantic grouping and rehearsal strategies on event-related brain potentials.

PubMed

Schleepen, T M J; Markus, C R; Jonkman, L M

2014-12-01

The application of elaborative encoding strategies during learning, such as grouping items on similar semantic categories, increases the likelihood of later recall. Previous studies have suggested that stimuli that encourage semantic grouping strategies had modulating effects on specific ERP components. However, these studies did not differentiate between ERP activation patterns evoked by elaborative working memory strategies like semantic grouping and more simple strategies like rote rehearsal. Identification of neurocognitive correlates underlying successful use of elaborative strategies is important to understand better why certain populations, like children or elderly people, have problems applying such strategies. To compare ERP activation during the application of elaborative versus more simple strategies subjects had to encode either four semantically related or unrelated pictures by respectively applying a semantic category grouping or a simple rehearsal strategy. Another goal was to investigate if maintenance of semantically grouped vs. ungrouped pictures modulated ERP-slow waves differently. At the behavioral level there was only a semantic grouping benefit in terms of faster responding on correct rejections (i.e. when the memory probe stimulus was not part of the memory set). At the neural level, during encoding semantic grouping only had a modest specific modulatory effect on a fronto-central Late Positive Component (LPC), emerging around 650 ms. Other ERP components (i.e. P200, N400 and a second Late Positive Component) that had been earlier related to semantic grouping encoding processes now showed stronger modulation by rehearsal than by semantic grouping. During maintenance semantic grouping had specific modulatory effects on left and right frontal slow wave activity. These results stress the importance of careful control of strategy use when investigating the neural correlates of elaborative encoding. Copyright © 2014 Elsevier B.V. All rights reserved.
Decoding semantic information from human electrocorticographic (ECoG) signals.

PubMed

Wang, Wei; Degenhart, Alan D; Sudre, Gustavo P; Pomerleau, Dean A; Tyler-Kabara, Elizabeth C

2011-01-01

This study examined the feasibility of decoding semantic information from human cortical activity. Four human subjects undergoing presurgical brain mapping and seizure foci localization participated in this study. Electrocorticographic (ECoG) signals were recorded while the subjects performed simple language tasks involving semantic information processing, such as a picture naming task where subjects named pictures of objects belonging to different semantic categories. Robust high-gamma band (60-120 Hz) activation was observed at the left inferior frontal gyrus (LIFG) and the posterior portion of the superior temporal gyrus (pSTG) with a temporal sequence corresponding to speech production and perception. Furthermore, Gaussian Naïve Bayes and Support Vector Machine classifiers, two commonly used machine learning algorithms for pattern recognition, were able to predict the semantic category of an object using cortical activity captured by ECoG electrodes covering the frontal, temporal and parietal cortices. These findings have implications for both basic neuroscience research and development of semantic-based brain-computer interface systems (BCI) that can help individuals with severe motor or communication disorders to express their intention and thoughts.
From perceptual to lexico-semantic analysis--cortical plasticity enabling new levels of processing.

PubMed

Schlaffke, Lara; Rüther, Naima N; Heba, Stefanie; Haag, Lauren M; Schultz, Thomas; Rosengarth, Katharina; Tegenthoff, Martin; Bellebaum, Christian; Schmidt-Wilcke, Tobias

2015-11-01

Certain kinds of stimuli can be processed on multiple levels. While the neural correlates of different levels of processing (LOPs) have been investigated to some extent, most of the studies involve skills and/or knowledge already present when performing the task. In this study we specifically sought to identify neural correlates of an evolving skill that allows the transition from perceptual to a lexico-semantic stimulus analysis. Eighteen participants were trained to decode 12 letters of Morse code that were presented acoustically inside and outside of the scanner environment. Morse code was presented in trains of three letters while brain activity was assessed with fMRI. Participants either attended to the stimulus length (perceptual analysis), or evaluated its meaning distinguishing words from nonwords (lexico-semantic analysis). Perceptual and lexico-semantic analyses shared a mutual network comprising the left premotor cortex, the supplementary motor area (SMA) and the inferior parietal lobule (IPL). Perceptual analysis was associated with a strong brain activation in the SMA and the superior temporal gyrus bilaterally (STG), which remained unaltered from pre and post training. In the lexico-semantic analysis post learning, study participants showed additional activation in the left inferior frontal cortex (IFC) and in the left occipitotemporal cortex (OTC), regions known to be critically involved in lexical processing. Our data provide evidence for cortical plasticity evolving with a learning process enabling the transition from perceptual to lexico-semantic stimulus analysis. Importantly, the activation pattern remains task-related LOP and is thus the result of a decision process as to which LOP to engage in. © 2015 The Authors. Human Brain Mapping Published by Wiley Periodicals, Inc.
Tracking neural coding of perceptual and semantic features of concrete nouns

PubMed Central

Sudre, Gustavo; Pomerleau, Dean; Palatucci, Mark; Wehbe, Leila; Fyshe, Alona; Salmelin, Riitta; Mitchell, Tom

2015-01-01

We present a methodological approach employing magnetoencephalography (MEG) and machine learning techniques to investigate the flow of perceptual and semantic information decodable from neural activity in the half second during which the brain comprehends the meaning of a concrete noun. Important information about the cortical location of neural activity related to the representation of nouns in the human brain has been revealed by past studies using fMRI. However, the temporal sequence of processing from sensory input to concept comprehension remains unclear, in part because of the poor time resolution provided by fMRI. In this study, subjects answered 20 questions (e.g. is it alive?) about the properties of 60 different nouns prompted by simultaneous presentation of a pictured item and its written name. Our results show that the neural activity observed with MEG encodes a variety of perceptual and semantic features of stimuli at different times relative to stimulus onset, and in different cortical locations. By decoding these features, our MEG-based classifier was able to reliably distinguish between two different concrete nouns that it had never seen before. The results demonstrate that there are clear differences between the time course of the magnitude of MEG activity and that of decodable semantic information. Perceptual features were decoded from MEG activity earlier in time than semantic features, and features related to animacy, size, and manipulability were decoded consistently across subjects. We also observed that regions commonly associated with semantic processing in the fMRI literature may not show high decoding results in MEG. We believe that this type of approach and the accompanying machine learning methods can form the basis for further modeling of the flow of neural information during language processing and a variety of other cognitive processes. PMID:22565201
From perceptual to lexico‐semantic analysis—cortical plasticity enabling new levels of processing

PubMed Central

Schlaffke, Lara; Rüther, Naima N.; Heba, Stefanie; Haag, Lauren M.; Schultz, Thomas; Rosengarth, Katharina; Tegenthoff, Martin; Bellebaum, Christian

2015-01-01

Abstract Certain kinds of stimuli can be processed on multiple levels. While the neural correlates of different levels of processing (LOPs) have been investigated to some extent, most of the studies involve skills and/or knowledge already present when performing the task. In this study we specifically sought to identify neural correlates of an evolving skill that allows the transition from perceptual to a lexico‐semantic stimulus analysis. Eighteen participants were trained to decode 12 letters of Morse code that were presented acoustically inside and outside of the scanner environment. Morse code was presented in trains of three letters while brain activity was assessed with fMRI. Participants either attended to the stimulus length (perceptual analysis), or evaluated its meaning distinguishing words from nonwords (lexico‐semantic analysis). Perceptual and lexico‐semantic analyses shared a mutual network comprising the left premotor cortex, the supplementary motor area (SMA) and the inferior parietal lobule (IPL). Perceptual analysis was associated with a strong brain activation in the SMA and the superior temporal gyrus bilaterally (STG), which remained unaltered from pre and post training. In the lexico‐semantic analysis post learning, study participants showed additional activation in the left inferior frontal cortex (IFC) and in the left occipitotemporal cortex (OTC), regions known to be critically involved in lexical processing. Our data provide evidence for cortical plasticity evolving with a learning process enabling the transition from perceptual to lexico‐semantic stimulus analysis. Importantly, the activation pattern remains task‐related LOP and is thus the result of a decision process as to which LOP to engage in. Hum Brain Mapp 36:4512–4528, 2015. © 2015 The Authors. Human Brain Mapping Published byWiley Periodicals, Inc. PMID:26304153
Semantic Indexing of Medical Learning Objects: Medical Students' Usage of a Semantic Network

PubMed Central

Gießler, Paul; Ohnesorge-Radtke, Ursula; Spreckelsen, Cord

2015-01-01

Background The Semantically Annotated Media (SAM) project aims to provide a flexible platform for searching, browsing, and indexing medical learning objects (MLOs) based on a semantic network derived from established classification systems. Primarily, SAM supports the Aachen emedia skills lab, but SAM is ready for indexing distributed content and the Simple Knowledge Organizing System standard provides a means for easily upgrading or even exchanging SAM’s semantic network. There is a lack of research addressing the usability of MLO indexes or search portals like SAM and the user behavior with such platforms. Objective The purpose of this study was to assess the usability of SAM by investigating characteristic user behavior of medical students accessing MLOs via SAM. Methods In this study, we chose a mixed-methods approach. Lean usability testing was combined with usability inspection by having the participants complete four typical usage scenarios before filling out a questionnaire. The questionnaire was based on the IsoMetrics usability inventory. Direct user interaction with SAM (mouse clicks and pages accessed) was logged. Results The study analyzed the typical usage patterns and habits of students using a semantic network for accessing MLOs. Four scenarios capturing characteristics of typical tasks to be solved by using SAM yielded high ratings of usability items and showed good results concerning the consistency of indexing by different users. Long-tail phenomena emerge as they are typical for a collaborative Web 2.0 platform. Suitable but nonetheless rarely used keywords were assigned to MLOs by some users. Conclusions It is possible to develop a Web-based tool with high usability and acceptance for indexing and retrieval of MLOs. SAM can be applied to indexing multicentered repositories of MLOs collaboratively. PMID:27731860
Semantic Indexing of Medical Learning Objects: Medical Students' Usage of a Semantic Network.

PubMed

Tix, Nadine; Gießler, Paul; Ohnesorge-Radtke, Ursula; Spreckelsen, Cord

2015-11-11

The Semantically Annotated Media (SAM) project aims to provide a flexible platform for searching, browsing, and indexing medical learning objects (MLOs) based on a semantic network derived from established classification systems. Primarily, SAM supports the Aachen emedia skills lab, but SAM is ready for indexing distributed content and the Simple Knowledge Organizing System standard provides a means for easily upgrading or even exchanging SAM's semantic network. There is a lack of research addressing the usability of MLO indexes or search portals like SAM and the user behavior with such platforms. The purpose of this study was to assess the usability of SAM by investigating characteristic user behavior of medical students accessing MLOs via SAM. In this study, we chose a mixed-methods approach. Lean usability testing was combined with usability inspection by having the participants complete four typical usage scenarios before filling out a questionnaire. The questionnaire was based on the IsoMetrics usability inventory. Direct user interaction with SAM (mouse clicks and pages accessed) was logged. The study analyzed the typical usage patterns and habits of students using a semantic network for accessing MLOs. Four scenarios capturing characteristics of typical tasks to be solved by using SAM yielded high ratings of usability items and showed good results concerning the consistency of indexing by different users. Long-tail phenomena emerge as they are typical for a collaborative Web 2.0 platform. Suitable but nonetheless rarely used keywords were assigned to MLOs by some users. It is possible to develop a Web-based tool with high usability and acceptance for indexing and retrieval of MLOs. SAM can be applied to indexing multicentered repositories of MLOs collaboratively.
The thematic hierarchy in sentence comprehension: A study on the interaction between verb class and word order in Spanish.

PubMed

Gattei, Carolina A; Dickey, Michael W; Wainselboim, Alejandro J; París, Luis

2015-01-01

Linking is the theory that captures the mapping of the semantic roles of lexical arguments to the syntactic functions of the phrases that realize them. At the sentence level, linking allows us to understand "who did what to whom" in an event. In Spanish, linking has been shown to interact with word order, verb class, and case marking. The current study aims to provide the first piece of experimental evidence about the interplay between word order and verb type in Spanish. We achieve this by adopting role and reference grammar and the extended argument dependency model. Two different types of clauses were examined in a self-paced reading task: clauses with object-experiencer psychological verbs and activity verbs. These types of verbs differ in the way that their syntactic and semantic structures are linked, and thus they provide interesting evidence on how information that belongs to the syntax-semantics interface might influence the predictive and integrative processes of sentence comprehension with alternative word orders. Results indicate that in Spanish, comprehension and processing speed is enhanced when the order of the constituents in the sentence mirrors their ranking on a semantic hierarchy that encodes a verb's lexical semantics. Moreover, results show that during online comprehension, predictive mechanisms based on argument hierarchization are used rapidly to inform the processing system. Our findings corroborate already existing cross-linguistic evidence on the issue and are briefly discussed in the light of other sentence-processing models.
Sharing Human-Generated Observations by Integrating HMI and the Semantic Sensor Web

PubMed Central

Sigüenza, Álvaro; Díaz-Pardo, David; Bernat, Jesús; Vancea, Vasile; Blanco, José Luis; Conejero, David; Gómez, Luis Hernández

2012-01-01

Current “Internet of Things” concepts point to a future where connected objects gather meaningful information about their environment and share it with other objects and people. In particular, objects embedding Human Machine Interaction (HMI), such as mobile devices and, increasingly, connected vehicles, home appliances, urban interactive infrastructures, etc., may not only be conceived as sources of sensor information, but, through interaction with their users, they can also produce highly valuable context-aware human-generated observations. We believe that the great promise offered by combining and sharing all of the different sources of information available can be realized through the integration of HMI and Semantic Sensor Web technologies. This paper presents a technological framework that harmonizes two of the most influential HMI and Sensor Web initiatives: the W3C's Multimodal Architecture and Interfaces (MMI) and the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) with its semantic extension, respectively. Although the proposed framework is general enough to be applied in a variety of connected objects integrating HMI, a particular development is presented for a connected car scenario where drivers' observations about the traffic or their environment are shared across the Semantic Sensor Web. For implementation and evaluation purposes an on-board OSGi (Open Services Gateway Initiative) architecture was built, integrating several available HMI, Sensor Web and Semantic Web technologies. A technical performance test and a conceptual validation of the scenario with potential users are reported, with results suggesting the approach is sound. PMID:22778643
OmniSearch: a semantic search system based on the Ontology for MIcroRNA Target (OMIT) for microRNA-target gene interaction data.

PubMed

Huang, Jingshan; Gutierrez, Fernando; Strachan, Harrison J; Dou, Dejing; Huang, Weili; Smith, Barry; Blake, Judith A; Eilbeck, Karen; Natale, Darren A; Lin, Yu; Wu, Bin; Silva, Nisansa de; Wang, Xiaowei; Liu, Zixing; Borchert, Glen M; Tan, Ming; Ruttenberg, Alan

2016-01-01

As a special class of non-coding RNAs (ncRNAs), microRNAs (miRNAs) perform important roles in numerous biological and pathological processes. The realization of miRNA functions depends largely on how miRNAs regulate specific target genes. It is therefore critical to identify, analyze, and cross-reference miRNA-target interactions to better explore and delineate miRNA functions. Semantic technologies can help in this regard. We previously developed a miRNA domain-specific application ontology, Ontology for MIcroRNA Target (OMIT), whose goal was to serve as a foundation for semantic annotation, data integration, and semantic search in the miRNA field. In this paper we describe our continuing effort to develop the OMIT, and demonstrate its use within a semantic search system, OmniSearch, designed to facilitate knowledge capture of miRNA-target interaction data. Important changes in the current version OMIT are summarized as: (1) following a modularized ontology design (with 2559 terms imported from the NCRO ontology); (2) encoding all 1884 human miRNAs (vs. 300 in previous versions); and (3) setting up a GitHub project site along with an issue tracker for more effective community collaboration on the ontology development. The OMIT ontology is free and open to all users, accessible at: http://purl.obolibrary.org/obo/omit.owl. The OmniSearch system is also free and open to all users, accessible at: http://omnisearch.soc.southalabama.edu/index.php/Software.
Sharing human-generated observations by integrating HMI and the Semantic Sensor Web.

PubMed

Sigüenza, Alvaro; Díaz-Pardo, David; Bernat, Jesús; Vancea, Vasile; Blanco, José Luis; Conejero, David; Gómez, Luis Hernández

2012-01-01

Current "Internet of Things" concepts point to a future where connected objects gather meaningful information about their environment and share it with other objects and people. In particular, objects embedding Human Machine Interaction (HMI), such as mobile devices and, increasingly, connected vehicles, home appliances, urban interactive infrastructures, etc., may not only be conceived as sources of sensor information, but, through interaction with their users, they can also produce highly valuable context-aware human-generated observations. We believe that the great promise offered by combining and sharing all of the different sources of information available can be realized through the integration of HMI and Semantic Sensor Web technologies. This paper presents a technological framework that harmonizes two of the most influential HMI and Sensor Web initiatives: the W3C's Multimodal Architecture and Interfaces (MMI) and the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) with its semantic extension, respectively. Although the proposed framework is general enough to be applied in a variety of connected objects integrating HMI, a particular development is presented for a connected car scenario where drivers' observations about the traffic or their environment are shared across the Semantic Sensor Web. For implementation and evaluation purposes an on-board OSGi (Open Services Gateway Initiative) architecture was built, integrating several available HMI, Sensor Web and Semantic Web technologies. A technical performance test and a conceptual validation of the scenario with potential users are reported, with results suggesting the approach is sound.
Creating Usage Context-Based Object Similarities to Boost Recommender Systems in Technology Enhanced Learning

ERIC Educational Resources Information Center

Niemann, Katja; Wolpers, Martin

2015-01-01

In this paper, we introduce a new way of detecting semantic similarities between learning objects by analysing their usage in web portals. Our approach relies on the usage-based relations between the objects themselves rather then on the content of the learning objects or on the relations between users and learning objects. We then take this new…
Semantically-enabled sensor plug & play for the sensor web.

PubMed

Bröring, Arne; Maúe, Patrick; Janowicz, Krzysztof; Nüst, Daniel; Malewski, Christian

2011-01-01

Environmental sensors have continuously improved by becoming smaller, cheaper, and more intelligent over the past years. As consequence of these technological advancements, sensors are increasingly deployed to monitor our environment. The large variety of available sensor types with often incompatible protocols complicates the integration of sensors into observing systems. The standardized Web service interfaces and data encodings defined within OGC's Sensor Web Enablement (SWE) framework make sensors available over the Web and hide the heterogeneous sensor protocols from applications. So far, the SWE framework does not describe how to integrate sensors on-the-fly with minimal human intervention. The driver software which enables access to sensors has to be implemented and the measured sensor data has to be manually mapped to the SWE models. In this article we introduce a Sensor Plug & Play infrastructure for the Sensor Web by combining (1) semantic matchmaking functionality, (2) a publish/subscribe mechanism underlying the SensorWeb, as well as (3) a model for the declarative description of sensor interfaces which serves as a generic driver mechanism. We implement and evaluate our approach by applying it to an oil spill scenario. The matchmaking is realized using existing ontologies and reasoning engines and provides a strong case for the semantic integration capabilities provided by Semantic Web research.
Semantically-Enabled Sensor Plug & Play for the Sensor Web

PubMed Central

Bröring, Arne; Maúe, Patrick; Janowicz, Krzysztof; Nüst, Daniel; Malewski, Christian

2011-01-01

Environmental sensors have continuously improved by becoming smaller, cheaper, and more intelligent over the past years. As consequence of these technological advancements, sensors are increasingly deployed to monitor our environment. The large variety of available sensor types with often incompatible protocols complicates the integration of sensors into observing systems. The standardized Web service interfaces and data encodings defined within OGC’s Sensor Web Enablement (SWE) framework make sensors available over the Web and hide the heterogeneous sensor protocols from applications. So far, the SWE framework does not describe how to integrate sensors on-the-fly with minimal human intervention. The driver software which enables access to sensors has to be implemented and the measured sensor data has to be manually mapped to the SWE models. In this article we introduce a Sensor Plug & Play infrastructure for the Sensor Web by combining (1) semantic matchmaking functionality, (2) a publish/subscribe mechanism underlying the SensorWeb, as well as (3) a model for the declarative description of sensor interfaces which serves as a generic driver mechanism. We implement and evaluate our approach by applying it to an oil spill scenario. The matchmaking is realized using existing ontologies and reasoning engines and provides a strong case for the semantic integration capabilities provided by Semantic Web research. PMID:22164033
A health analytics semantic ETL service for obesity surveillance.

PubMed

Poulymenopoulou, M; Papakonstantinou, D; Malamateniou, F; Vassilacopoulos, G

2015-01-01

The increasingly large amount of data produced in healthcare (e.g. collected through health information systems such as electronic medical records - EMRs or collected through novel data sources such as personal health records - PHRs, social media, web resources) enable the creation of detailed records about people's health, sentiments and activities (e.g. physical activity, diet, sleep quality) that can be used in the public health area among others. However, despite the transformative potential of big data in public health surveillance there are several challenges in integrating big data. In this paper, the interoperability challenge is tackled and a semantic Extract Transform Load (ETL) service is proposed that seeks to semantically annotate big data to result into valuable data for analysis. This service is considered as part of a health analytics engine on the cloud that interacts with existing healthcare information exchange networks, like the Integrating the Healthcare Enterprise (IHE), PHRs, sensors, mobile applications, and other web resources to retrieve patient health, behavioral and daily activity data. The semantic ETL service aims at semantically integrating big data for use by analytic mechanisms. An illustrative implementation of the service on big data which is potentially relevant to human obesity, enables using appropriate analytic techniques (e.g. machine learning, text mining) that are expected to assist in identifying patterns and contributing factors (e.g. genetic background, social, environmental) for this social phenomenon and, hence, drive health policy changes and promote healthy behaviors where residents live, work, learn, shop and play.
An Enhanced Personal Learning Environment Using Social Semantic Web Technologies

ERIC Educational Resources Information Center

Halimi, Khaled; Seridi-Bouchelaghem, Hassina; Faron-Zucker, Catherine

2014-01-01

Compared with learning in classrooms, classical e-learning systems are less adaptive and once a system that supports a particular strategy has been designed and implemented, it is less likely to change according to student's interactions and preferences. Remote educational systems should be developed to ensure as much as necessary the…

Assessing Assessment: In Pursuit of Meaningful Learning

ERIC Educational Resources Information Center

Rootman-le Grange, Ilse; Blackie, Margaret A. L.

2018-01-01

The challenge of supporting the development of meaningful learning is prevalent in chemistry education research. One of the core activities used in the learning process is assessments. The aim of this paper is to illustrate how the semantics dimension of Legitimation Code Theory can be a helpful tool to critique the quality of assessments and…
Semantic Overlays in Educational Content Networks--The hylOs Approach

ERIC Educational Resources Information Center

Engelhardt, Michael; Hildebrand, Arne; Lange, Dagmar; Schmidt, Thomas C.

2006-01-01

Purpose: The paper aims to introduce an educational content management system, Hypermedia Learning Objects System (hylOs), which is fully compliant to the IEEE LOM eLearning object metadata standard. Enabled through an advanced authoring toolset, hylOs allows the definition of instructional overlays of a given eLearning object mesh.…
Adaptive Synchronization of Semantically Compressed Instructional Videos for Collaborative Distance Learning

ERIC Educational Resources Information Center

Phung, Dan; Valetto, Giuseppe; Kaiser, Gail E.; Liu, Tiecheng; Kender, John R.

2007-01-01

The increasing popularity of online courses has highlighted the need for collaborative learning tools for student groups. In this article, we present an e-Learning architecture and adaptation model called AI2TV (Adaptive Interactive Internet Team Video), which allows groups of students to collaboratively view instructional videos in synchrony.…
A Practical Ontology Query Expansion Algorithm for Semantic-Aware Learning Objects Retrieval

ERIC Educational Resources Information Center

Lee, Ming-Che; Tsai, Kun Hua; Wang, Tzone I.

2008-01-01

Following the rapid development of Internet, particularly web page interaction technology, distant e-learning has become increasingly realistic and popular. To solve the problems associated with sharing and reusing teaching materials in different e-learning systems, several standard formats, including SCORM, IMS, LOM, and AICC, etc., recently have…
Rules and Construction Effects in Learning the Argument Structure of Verbs

ERIC Educational Resources Information Center

Demuth, Katherine; Machobane, 'Malillo; Moloi, Francina

2003-01-01

Theorists of language acquisition have long debated the means by which children learn the argument structure of verbs (e.g. Bowerman, 1974, 1990; Pinker, 1984, 1989; Tomasello, 1992). Central to this controversy has been the possible role of verb semantics, especially in learning which verbs undergo dative-shift alternation in languages like…
Judging Words by Their Covers and the Company They Keep: Probabilistic Cues Support Word Learning

ERIC Educational Resources Information Center

Lany, Jill

2014-01-01

Statistical learning may be central to lexical and grammatical development. The phonological and distributional properties of words provide probabilistic cues to their grammatical and semantic properties. Infants can capitalize on such probabilistic cues to learn grammatical patterns in listening tasks. However, infants often struggle to learn…
Model-Based Learning: A Synthesis of Theory and Research

ERIC Educational Resources Information Center

Seel, Norbert M.

2017-01-01

This article provides a review of theoretical approaches to model-based learning and related research. In accordance with the definition of model-based learning as an acquisition and utilization of mental models by learners, the first section centers on mental model theory. In accordance with epistemology of modeling the issues of semantics,…
An Ontology Infrastructure for an E-Learning Scenario

ERIC Educational Resources Information Center

Guo, Wen-Ying; Chen, De-Ren

2007-01-01

Selecting appropriate learning services for a learner from a large number of heterogeneous knowledge sources is a complex and challenging task. This article illustrates and discusses how Semantic Web technologies such as RDF [resource description framework] and ontology can be applied to e-learning systems to help the learner in selecting an…
A Framework and a Methodology for Developing Authentic Constructivist e-Learning Environments

ERIC Educational Resources Information Center

Zualkernan, Imran A.

2006-01-01

Semantically rich domains require operative knowledge to solve complex problems in real-world settings. These domains provide an ideal environment for developing authentic constructivist e-learning environments. In this paper we present a framework and a methodology for developing authentic learning environments for such domains. The framework is…
How Do Korsakoff Patients Learn New Concepts?

ERIC Educational Resources Information Center

Pitel, Anne Lise; Beaunieux, Helene; Guillery-Girard, Berengere; Witkowski, Thomas; de la Sayette, Vincent; Viader, Fausto; Desgranges, Beatrice; Eustache, Francis

2009-01-01

The goal of the present investigation was to assess semantic learning in Korsakoff patients (KS), compared with uncomplicated alcoholics (AL) and control subjects (CS), taking the nature of the information to-be-learned and the episodic memory profiles of the three groups into account. Ten new complex concepts, each illustrated by a photo and…
Using deep learning for content-based medical image retrieval

NASA Astrophysics Data System (ADS)

Sun, Qinpei; Yang, Yuanyuan; Sun, Jianyong; Yang, Zhiming; Zhang, Jianguo

2017-03-01

Content-Based medical image retrieval (CBMIR) is been highly active research area from past few years. The retrieval performance of a CBMIR system crucially depends on the feature representation, which have been extensively studied by researchers for decades. Although a variety of techniques have been proposed, it remains one of the most challenging problems in current CBMIR research, which is mainly due to the well-known "semantic gap" issue that exists between low-level image pixels captured by machines and high-level semantic concepts perceived by human[1]. Recent years have witnessed some important advances of new techniques in machine learning. One important breakthrough technique is known as "deep learning". Unlike conventional machine learning methods that are often using "shallow" architectures, deep learning mimics the human brain that is organized in a deep architecture and processes information through multiple stages of transformation and representation. This means that we do not need to spend enormous energy to extract features manually. In this presentation, we propose a novel framework which uses deep learning to retrieval the medical image to improve the accuracy and speed of a CBIR in integrated RIS/PACS.
BIBLIOGRAPHY ON VERBAL LEARNING.

ERIC Educational Resources Information Center

Harvard Univ., Cambridge, MA. Graduate School of Education.

THIS BIBLIOGRAPHY LISTS MATERIAL ON VARIOUS ASPECTS OF VERBAL LEARNING. APPROXIMATELY 50 UNANNOTATED REFERENCES ARE PROVIDED TO DOCUMENTS DATING FROM 1960 TO 1965. JOURNALS, BOOKS, AND REPORT MATERIALS ARE LISTED. SUBJECT AREAS INCLUDED ARE CONDITIONING, VERBAL BEHAVIOR, PROBLEM SOLVING, SEMANTIC SATIATION, STIMULUS DURATION, AND VERBAL…
Effect of normal aging and of Alzheimer's disease on, episodic memory.

PubMed

Le Moal, S; Reymann, J M; Thomas, V; Cattenoz, C; Lieury, A; Allain, H

1997-01-01

Performances of 12 patients with Alzheimer's disease (AD), 15 healthy elderly subjects and 20 young healthy volunteers were compared on two episodic memory tests. The first, a learning test of semantically related words, enabled an assessment of the effect of semantic relationships on word learning by controlling the encoding and retrieval processes. The second, a dual coding test, is about the assessment of automatic processes operating during drawings encoding. The results obtained demonstrated quantitative and qualitative differences between the population. Manifestations of episodic memory deficit in AD patients were shown not only by lower performance scores than in elderly controls, but also by the lack of any effect of semantic cues and the production of a large number of extra-list intrusions. Automatic processes underlying dual coding appear to be spared in AD, although more time is needed to process information than in young or elderly subjects. These findings confirm former data and emphasize the preservation of certain memory processes (dual coding) in AD which could be used in future therapeutic approaches.
Learning to Fail in Aphasia: An Investigation of Error Learning in Naming

PubMed Central

Middleton, Erica L.; Schwartz, Myrna F.

2013-01-01

Purpose To determine if the naming impairment in aphasia is influenced by error learning and if error learning is related to type of retrieval strategy. Method Nine participants with aphasia and ten neurologically-intact controls named familiar proper noun concepts. When experiencing tip-of-the-tongue naming failure (TOT) in an initial TOT-elicitation phase, participants were instructed to adopt phonological or semantic self-cued retrieval strategies. In the error learning manipulation, items evoking TOT states during TOT-elicitation were randomly assigned to a short or long time condition where participants were encouraged to continue to try to retrieve the name for either 20 seconds (short interval) or 60 seconds (long). The incidence of TOT on the same items was measured on a post test after 48-hours. Error learning was defined as a higher rate of recurrent TOTs (TOT at both TOT-elicitation and post test) for items assigned to the long (versus short) time condition. Results In the phonological condition, participants with aphasia showed error learning whereas controls showed a pattern opposite to error learning. There was no evidence for error learning in the semantic condition for either group. Conclusion Error learning is operative in aphasia, but dependent on the type of strategy employed during naming failure. PMID:23816662
Weakly supervised visual dictionary learning by harnessing image attributes.

PubMed

Gao, Yue; Ji, Rongrong; Liu, Wei; Dai, Qionghai; Hua, Gang

2014-12-01

Bag-of-features (BoFs) representation has been extensively applied to deal with various computer vision applications. To extract discriminative and descriptive BoF, one important step is to learn a good dictionary to minimize the quantization loss between local features and codewords. While most existing visual dictionary learning approaches are engaged with unsupervised feature quantization, the latest trend has turned to supervised learning by harnessing the semantic labels of images or regions. However, such labels are typically too expensive to acquire, which restricts the scalability of supervised dictionary learning approaches. In this paper, we propose to leverage image attributes to weakly supervise the dictionary learning procedure without requiring any actual labels. As a key contribution, our approach establishes a generative hidden Markov random field (HMRF), which models the quantized codewords as the observed states and the image attributes as the hidden states, respectively. Dictionary learning is then performed by supervised grouping the observed states, where the supervised information is stemmed from the hidden states of the HMRF. In such a way, the proposed dictionary learning approach incorporates the image attributes to learn a semantic-preserving BoF representation without any genuine supervision. Experiments in large-scale image retrieval and classification tasks corroborate that our approach significantly outperforms the state-of-the-art unsupervised dictionary learning approaches.
Reciprocal Effects of Self-Regulation, Semantic Knowledge, and Reading Comprehension in Early Elementary School

PubMed Central

Connor, Carol McDonald; Day, Stephanie L.; Phillips, Beth; Sparapani, Nicole; Ingebrand, Sarah W.; McLean, Leigh; Barrus, Angela; Kaschak, Michael P.

2016-01-01

Many assume that cognitive and linguistic processes, such as semantic knowledge (SK) and self-regulation (SR) subserve learned skills like reading. However, complex models of interacting and bootstrapping effects of SK, SR, instruction, and reading hypothesize reciprocal effects. Testing this “lattice” model with children (n = 852) followed from 1st–2nd grade (5.9–10.4 years-of-age), revealed reciprocal effects for reading and SR, and reading and SK, but not SR and SK. More effective literacy instruction reduced reading stability over time. Findings elucidate the synergistic and reciprocal effects of learning to read on other important linguistic, self-regulatory, and cognitive processes, the value of using complex models of development to inform intervention design, and how learned skills may influence development during middle childhood. PMID:27264645
Real-time object detection and semantic segmentation for autonomous driving

NASA Astrophysics Data System (ADS)

Li, Baojun; Liu, Shun; Xu, Weichao; Qiu, Wei

2018-02-01

In this paper, we proposed a Highly Coupled Network (HCNet) for joint objection detection and semantic segmentation. It follows that our method is faster and performs better than the previous approaches whose decoder networks of different tasks are independent. Besides, we present multi-scale loss architecture to learn better representation for different scale objects, but without extra time in the inference phase. Experiment results show that our method achieves state-of-the-art results on the KITTI datasets. Moreover, it can run at 35 FPS on a GPU and thus is a practical solution to object detection and semantic segmentation for autonomous driving.
Empirical Distributional Semantics: Methods and Biomedical Applications

PubMed Central

Cohen, Trevor; Widdows, Dominic

2009-01-01

Over the past fifteen years, a range of methods have been developed that are able to learn human-like estimates of the semantic relatedness between terms from the way in which these terms are distributed in a corpus of unannotated natural language text. These methods have also been evaluated in a number of applications in the cognitive science, computational linguistics and the information retrieval literatures. In this paper, we review the available methodologies for derivation of semantic relatedness from free text, as well as their evaluation in a variety of biomedical and other applications. Recent methodological developments, and their applicability to several existing applications are also discussed. PMID:19232399
Integrating the automatic and the controlled: Strategies in Semantic Priming in an Attractor Network with Latching Dynamics

PubMed Central

Lerner, Itamar; Bentin, Shlomo; Shriki, Oren

2014-01-01

Semantic priming has long been recognized to reflect, along with automatic semantic mechanisms, the contribution of controlled strategies. However, previous theories of controlled priming were mostly qualitative, lacking common grounds with modern mathematical models of automatic priming based on neural networks. Recently, we have introduced a novel attractor network model of automatic semantic priming with latching dynamics. Here, we extend this work to show how the same model can also account for important findings regarding controlled processes. Assuming the rate of semantic transitions in the network can be adapted using simple reinforcement learning, we show how basic findings attributed to controlled processes in priming can be achieved, including their dependency on stimulus onset asynchrony and relatedness proportion and their unique effect on associative, category-exemplar, mediated and backward prime-target relations. We discuss how our mechanism relates to the classic expectancy theory and how it can be further extended in future developments of the model. PMID:24890261
Learning semantic histopathological representation for basal cell carcinoma classification

NASA Astrophysics Data System (ADS)

Gutiérrez, Ricardo; Rueda, Andrea; Romero, Eduardo

2013-03-01

Diagnosis of a histopathology glass slide is a complex process that involves accurate recognition of several structures, their function in the tissue and their relation with other structures. The way in which the pathologist represents the image content and the relations between those objects yields a better and accurate diagnoses. Therefore, an appropriate semantic representation of the image content will be useful in several analysis tasks such as cancer classification, tissue retrieval and histopahological image analysis, among others. Nevertheless, to automatically recognize those structures and extract their inner semantic meaning are still very challenging tasks. In this paper we introduce a new semantic representation that allows to describe histopathological concepts suitable for classification. The approach herein identify local concepts using a dictionary learning approach, i.e., the algorithm learns the most representative atoms from a set of random sampled patches, and then models the spatial relations among them by counting the co-occurrence between atoms, while penalizing the spatial distance. The proposed approach was compared with a bag-of-features representation in a tissue classification task. For this purpose, 240 histological microscopical fields of view, 24 per tissue class, were collected. Those images fed a Support Vector Machine classifier per class, using 120 images as train set and the remaining ones for testing, maintaining the same proportion of each concept in the train and test sets. The obtained classification results, averaged from 100 random partitions of training and test sets, shows that our approach is more sensitive in average than the bag-of-features representation in almost 6%.

A Long-Term Retention Advantage for Spatial Information Learned Naturally and in the Laboratory

DTIC Science & Technology

1989-06-09

study. Memory & Cognition, 10, 324-332. Tulving, E. (1972). Episodic and semantic memory . In E. Tulving & W. Donaldson (Eds.), Organization of memory ...the cognitive research literature. Some of the better known divisions include the distinction between semantic , episodic and procedural knowledge...the probe method by being more structured and more focused on specific memory episodes . In general, questionnaire studies simply involve formulating
Lexico-Semantic Errors of the Learners of English: A Survey of Standard Seven Keiyo-Speaking Primary School Pupils in Keiyo District, Kenya

ERIC Educational Resources Information Center

Jeptarus, Kipsamo E.; Ngene, Patrick K.

2016-01-01

The purpose of this research was to study the Lexico-semantic errors of the Keiyo-speaking standard seven primary school learners of English as a Second Language (ESL) in Keiyo District, Kenya. This study was guided by two related theories: Error Analysis Theory/Approach by Corder (1971) which approaches L2 learning through a detailed analysis of…
Integrating Statistical Machine Learning in a Semantic Sensor Web for Proactive Monitoring and Control.

PubMed

Adeleke, Jude Adekunle; Moodley, Deshendran; Rens, Gavin; Adewumi, Aderemi Oluyinka

2017-04-09

Proactive monitoring and control of our natural and built environments is important in various application scenarios. Semantic Sensor Web technologies have been well researched and used for environmental monitoring applications to expose sensor data for analysis in order to provide responsive actions in situations of interest. While these applications provide quick response to situations, to minimize their unwanted effects, research efforts are still necessary to provide techniques that can anticipate the future to support proactive control, such that unwanted situations can be averted altogether. This study integrates a statistical machine learning based predictive model in a Semantic Sensor Web using stream reasoning. The approach is evaluated in an indoor air quality monitoring case study. A sliding window approach that employs the Multilayer Perceptron model to predict short term PM 2 . 5 pollution situations is integrated into the proactive monitoring and control framework. Results show that the proposed approach can effectively predict short term PM 2 . 5 pollution situations: precision of up to 0.86 and sensitivity of up to 0.85 is achieved over half hour prediction horizons, making it possible for the system to warn occupants or even to autonomously avert the predicted pollution situations within the context of Semantic Sensor Web.
Integrating Statistical Machine Learning in a Semantic Sensor Web for Proactive Monitoring and Control

PubMed Central

Adeleke, Jude Adekunle; Moodley, Deshendran; Rens, Gavin; Adewumi, Aderemi Oluyinka

2017-01-01

Proactive monitoring and control of our natural and built environments is important in various application scenarios. Semantic Sensor Web technologies have been well researched and used for environmental monitoring applications to expose sensor data for analysis in order to provide responsive actions in situations of interest. While these applications provide quick response to situations, to minimize their unwanted effects, research efforts are still necessary to provide techniques that can anticipate the future to support proactive control, such that unwanted situations can be averted altogether. This study integrates a statistical machine learning based predictive model in a Semantic Sensor Web using stream reasoning. The approach is evaluated in an indoor air quality monitoring case study. A sliding window approach that employs the Multilayer Perceptron model to predict short term PM2.5 pollution situations is integrated into the proactive monitoring and control framework. Results show that the proposed approach can effectively predict short term PM2.5 pollution situations: precision of up to 0.86 and sensitivity of up to 0.85 is achieved over half hour prediction horizons, making it possible for the system to warn occupants or even to autonomously avert the predicted pollution situations within the context of Semantic Sensor Web. PMID:28397776
Effects of donepezil on verbal memory after semantic processing in healthy older adults.

PubMed

FitzGerald, David B; Crucian, Gregory P; Mielke, Jeannine B; Shenal, Brian V; Burks, David; Womack, Kyle B; Ghacibeh, Georges; Drago, Valeria; Foster, Paul S; Valenstein, Edward; Heilman, Kenneth M

2008-06-01

To learn if acetylcholinesterase inhibitors alter verbal recall by improving semantic encoding in a double-blind randomized placebo-controlled trial. Cholinergic supplementation has been shown to improve delayed recall in adults with Alzheimer disease. With functional magnetic resonance imaging, elderly adults, when compared with younger participants, have reduced cortical activation with semantic processing. There have been no studies investigating the effects of cholinergic supplementation on semantic encoding in healthy elderly adults. Twenty elderly participants (mean age 71.5, SD+/-5.2) were recruited. All underwent memory testing before and after receiving donepezil (5 mg, n=11 or 10 mg, n=1) or placebo (n=8) for 6 weeks. Memory was tested using a Levels of Processing task, where a series of words are presented serially. Subjects were either asked to count consonants in a word (superficially process) or decide if the word was "pleasant" or "unpleasant" (semantically process). After 6 weeks of donepezil or placebo treatment, immediate and delayed recall of superficially and semantically processed words was compared with baseline performance. Immediate and delayed recall of superficially processed words did not show significant changes in either treatment group. With semantic processing, both immediate and delayed recall performance improved in the donepezil group. Our results suggest that when using semantic encoding, older normal subjects may be aided by anticholinesterase treatment. However, this treatment does not improve recall of superficially encoded words.
Word Writing vs. Meaning Inferencing in Contextualized L2 Vocabulary Learning: Assessing the Effect of Different Vocabulary Learning Strategies

ERIC Educational Resources Information Center

Candry, Sarah; Elgort, Irina; Deconinck, Julie; Eyckmans, June

2017-01-01

The majority of L2 vocabulary studies concentrate on learning word meaning and provide learners with opportunities for semantic elaboration (i.e., focus on word meaning). However, in initial vocabulary learning, engaging in structural elaboration (i.e., focus on word form) with a view to acquiring L2 word form is equally important. The present…
Semantic orchestration of image processing services for environmental analysis

NASA Astrophysics Data System (ADS)

Ranisavljević, Élisabeth; Devin, Florent; Laffly, Dominique; Le Nir, Yannick

2013-09-01

In order to analyze environmental dynamics, a major process is the classification of the different phenomena of the site (e.g. ice and snow for a glacier). When using in situ pictures, this classification requires data pre-processing. Not all the pictures need the same sequence of processes depending on the disturbances. Until now, these sequences have been done manually, which restricts the processing of large amount of data. In this paper, we present how to realize a semantic orchestration to automate the sequencing for the analysis. It combines two advantages: solving the problem of the amount of processing, and diversifying the possibilities in the data processing. We define a BPEL description to express the sequences. This BPEL uses some web services to run the data processing. Each web service is semantically annotated using an ontology of image processing. The dynamic modification of the BPEL is done using SPARQL queries on these annotated web services. The results obtained by a prototype implementing this method validate the construction of the different workflows that can be applied to a large number of pictures.
Semantic organizational strategy predicts verbal memory and remission rate of geriatric depression.

PubMed

Morimoto, Sarah Shizuko; Gunning, Faith M; Kanellopoulos, Dora; Murphy, Christopher F; Klimstra, Sibel A; Kelly, Robert E; Alexopoulos, George S

2012-05-01

This study tests the hypothesis that the use of semantic organizational strategy during the free-recall phase of a verbal memory task predicts remission of geriatric depression. Sixty-five older patients with major depression participated in a 12-week escitalopram treatment trial. Neuropsychological performance was assessed at baseline after a 2-week drug washout period. The Hopkins Verbal Learning Test-Revised was used to assess verbal learning and memory. Remission was defined as a Hamilton Depression Rating Scale score of ≤ 7 for 2 consecutive weeks and no longer meeting the DSM-IV-TR criteria for major depression. The association between the number of clusters used at the final learning trial (trial 3) and remission was examined using Cox's proportional hazards survival analysis. The relationship between the number of clusters utilized in the final learning trial and the number of words recalled after a 25-min delay was examined in a regression with age and education as covariates. Higher number of clusters utilized predicted remission rates (hazard ratio, 1.26 (95% confidence interval, 1.04-1.54); χ(2) = 4.23, df = 3, p = 0.04). There was a positive relationship between the total number of clusters used by the end of the third learning trial and the total number of words recalled at the delayed recall trial (F(3,58) = 7.93; p < 0.001). Effective semantic strategy use at baseline on a verbal list learning task by older depressed patients was associated with higher rates of remission with antidepressant treatment. This result provides support for previous findings indicating that measures of executive functioning at baseline are useful in predicting antidepressant response. Copyright © 2011 John Wiley & Sons, Ltd.
A Linked Data-Based Collaborative Annotation System for Increasing Learning Achievements

ERIC Educational Resources Information Center

Zarzour, Hafed; Sellami, Mokhtar

2017-01-01

With the emergence of the Web 2.0, collaborative annotation practices have become more mature in the field of learning. In this context, several recent studies have shown the powerful effects of the integration of annotation mechanism in learning process. However, most of these studies provide poor support for semantically structured resources,…
A Theoretical Analysis of Learning with Graphics--Implications for Computer Graphics Design.

ERIC Educational Resources Information Center

ChanLin, Lih-Juan

This paper reviews the literature pertinent to learning with graphics. The dual coding theory provides explanation about how graphics are stored and precessed in semantic memory. The level of processing theory suggests how graphics can be employed in learning to encourage deeper processing. In addition to dual coding theory and level of processing…
A Learning Design Ontology Based on the IMS Specification

ERIC Educational Resources Information Center

Amorim, Ricardo R.; Lama, Manuel; Sanchez, Eduardo; Riera, Adolfo; Vila, Xose A.

2006-01-01

In this paper, we present an ontology to represent the semantics of the IMS Learning Design (IMS LD) specification, a meta-language used to describe the main elements of the learning design process. The motivation of this work relies on the expressiveness limitations found on the current XML-Schema implementation of the IMS LD conceptual model. To…
Semantic Annotation of Resources to Learn with Connected Things

ERIC Educational Resources Information Center

Bouchereau, Aymeric; Roxin, Ioan

2017-01-01

Computer systems tend to be ubiquitous as they become more integrated in our everyday activities, embedded in tables, shoes, watch and plenty of others connected things (CT). In the e-learning field, the transformations induced by the Internet of Things (IoT) allow individuals to learn whenever they want, accessing a quantity of diverse digital…
When does word meaning affect immediate serial recall in semantic dementia?

PubMed

Jefferies, Elizabeth; Jones, Roy; Bateman, David; Ralph, Matthew A Lambon

2004-03-01

Patients with semantic dementia can show superior immediate recall of words that they still understand relatively well, as compared with more semantically degraded words, suggesting that conceptual knowledge makes a major contribution to phonological short-term memory. However, a number of studies have failed to show such a recall difference, challenging this view. We examined the effect of several methodological factors on the recall of known and degraded words in 4 patients with semantic dementia, in order to investigate possible reasons for this discrepancy. In general, our patients did exhibit poorer recall of the degraded words and made more phonological errors on these items. In addition, set size affected the magnitude of the recall advantage for known words. This finding suggests that semantic degradation influenced the rate of learning in the immediate recall task when the same items were presented repeatedly. The methods used to select known and degraded items also impacted on the recall difference. List length, however, did not affect the advantage for known words. The coherence of items in phonological short-term memory was affected by their semantic status, but not by the length of the material to be retained. The implications of these findings for the role of semantic and phonological representations in verbal short-term memory are discussed.
Semantic Segmentation of Indoor Point Clouds Using Convolutional Neural Network

NASA Astrophysics Data System (ADS)

Babacan, K.; Chen, L.; Sohn, G.

2017-11-01

As Building Information Modelling (BIM) thrives, geometry becomes no longer sufficient; an ever increasing variety of semantic information is needed to express an indoor model adequately. On the other hand, for the existing buildings, automatically generating semantically enriched BIM from point cloud data is in its infancy. The previous research to enhance the semantic content rely on frameworks in which some specific rules and/or features that are hand coded by specialists. These methods immanently lack generalization and easily break in different circumstances. On this account, a generalized framework is urgently needed to automatically and accurately generate semantic information. Therefore we propose to employ deep learning techniques for the semantic segmentation of point clouds into meaningful parts. More specifically, we build a volumetric data representation in order to efficiently generate the high number of training samples needed to initiate a convolutional neural network architecture. The feedforward propagation is used in such a way to perform the classification in voxel level for achieving semantic segmentation. The method is tested both for a mobile laser scanner point cloud, and a larger scale synthetically generated data. We also demonstrate a case study, in which our method can be effectively used to leverage the extraction of planar surfaces in challenging cluttered indoor environments.
Personalized E- learning System Based on Intelligent Agent

NASA Astrophysics Data System (ADS)

Duo, Sun; Ying, Zhou Cai

Lack of personalized learning is the key shortcoming of traditional e-Learning system. This paper analyzes the personal characters in e-Learning activity. In order to meet the personalized e-learning, a personalized e-learning system based on intelligent agent was proposed and realized in the paper. The structure of system, work process, the design of intelligent agent and the realization of intelligent agent were introduced in the paper. After the test use of the system by certain network school, we found that the system could improve the learner's initiative participation, which can provide learners with personalized knowledge service. Thus, we thought it might be a practical solution to realize self- learning and self-promotion in the lifelong education age.
What Research Says about Vocabulary Instruction for Students with Learning Disabilities

ERIC Educational Resources Information Center

Jitendra, Asha K.; Edwards, Lana L.; Sacks, Gabriell; Jacobson, Lisa A.

2004-01-01

This article summarizes published research on vocabulary instruction involving students with learning disabilities. Nineteen vocabulary studies that comprised 27 investigations were located. Study interventions gleaned from the review included keyword or mnemonic approaches, cognitive strategy instruction (e.g., semantic features analysis), direct…
Interaction between episodic and semantic memory networks in the acquisition and consolidation of novel spoken words.

PubMed

Takashima, Atsuko; Bakker, Iske; van Hell, Janet G; Janzen, Gabriele; McQueen, James M

2017-04-01

When a novel word is learned, its memory representation is thought to undergo a process of consolidation and integration. In this study, we tested whether the neural representations of novel words change as a function of consolidation by observing brain activation patterns just after learning and again after a delay of one week. Words learned with meanings were remembered better than those learned without meanings. Both episodic (hippocampus-dependent) and semantic (dependent on distributed neocortical areas) memory systems were utilised during recognition of the novel words. The extent to which the two systems were involved changed as a function of time and the amount of associated information, with more involvement of both systems for the meaningful words than for the form-only words after the one-week delay. These results suggest that the reason the meaningful words were remembered better is that their retrieval can benefit more from these two complementary memory systems. Copyright © 2016 Elsevier Inc. All rights reserved.
Sleep Benefits Memory for Semantic Category Structure While Preserving Exemplar-Specific Information.

PubMed

Schapiro, Anna C; McDevitt, Elizabeth A; Chen, Lang; Norman, Kenneth A; Mednick, Sara C; Rogers, Timothy T

2017-11-01

Semantic memory encompasses knowledge about both the properties that typify concepts (e.g. robins, like all birds, have wings) as well as the properties that individuate conceptually related items (e.g. robins, in particular, have red breasts). We investigate the impact of sleep on new semantic learning using a property inference task in which both kinds of information are initially acquired equally well. Participants learned about three categories of novel objects possessing some properties that were shared among category exemplars and others that were unique to an exemplar, with exposure frequency varying across categories. In Experiment 1, memory for shared properties improved and memory for unique properties was preserved across a night of sleep, while memory for both feature types declined over a day awake. In Experiment 2, memory for shared properties improved across a nap, but only for the lower-frequency category, suggesting a prioritization of weakly learned information early in a sleep period. The increase was significantly correlated with amount of REM, but was also observed in participants who did not enter REM, suggesting involvement of both REM and NREM sleep. The results provide the first evidence that sleep improves memory for the shared structure of object categories, while simultaneously preserving object-unique information.
Modulation of the inter-hemispheric processing of semantic information during normal aging. A divided visual field experiment.

PubMed

Hoyau, E; Cousin, E; Jaillard, A; Baciu, M

2016-12-01

We evaluated the effect of normal aging on the inter-hemispheric processing of semantic information by using the divided visual field (DVF) method, with words and pictures. Two main theoretical models have been considered, (a) the HAROLD model which posits that aging is associated with supplementary recruitment of the right hemisphere (RH) and decreased hemispheric specialization, and (b) the RH decline theory, which assumes that the RH becomes less efficient with aging, associated with increased LH specialization. Two groups of subjects were examined, a Young Group (YG) and an Old Group (OG), while participants performed a semantic categorization task (living vs. non-living) in words and pictures. The DVF was realized in two steps: (a) unilateral DVF presentation with stimuli presented separately in each visual field, left or right, allowing for their initial processing by only one hemisphere, right or left, respectively; (b) bilateral DVF presentation (BVF) with stimuli presented simultaneously in both visual fields, followed by their processing by both hemispheres. These two types of presentation permitted the evaluation of two main characteristics of the inter-hemispheric processing of information, the hemispheric specialization (HS) and the inter-hemispheric cooperation (IHC). Moreover, the BVF allowed determining the driver-hemisphere for processing information presented in BVF. Results obtained in OG indicated that: (a) semantic categorization was performed as accurately as YG, even if more slowly, (b) a non-semantic RH decline was observed, and (c) the LH controls the semantic processing during the BVF, suggesting an increased role of the LH in aging. However, despite the stronger involvement of the LH in OG, the RH is not completely devoid of semantic abilities. As discussed in the paper, neither the HAROLD nor the RH decline does fully explain this pattern of results. We rather suggest that the effect of aging on the hemispheric specialization and inter-hemispheric cooperation during semantic processing is explained not by only one model, but by an interaction between several complementary mechanisms and models. Copyright © 2015 Elsevier Ltd. All rights reserved.
Final Report of the MLA Committee on Lifelong Learning: Helping Adults Return to Learning To Improve Their Employment Potential and Realize Their Career Goals.

ERIC Educational Resources Information Center

Alberta Learning, Edmonton.

In 1999, the Committee on Lifelong Learning of the Ministry of Learning in Alberta, Canada, conducted a series of consultations on lifelong learning to identify ways of helping adults return to learning to improve their employment potential and realize their career goals. The committee received input from more than 450 Albertans in 14 rural and…

Procedurally Mediated Social Inferences: The Case of Category Accessibility Effects.

DTIC Science & Technology

1984-12-01

New York: Academic. Craik , F. I. M., & Lockhart , R. S. (1972). Levels of processing : A framework for memory research. Journal of Verbal Learning...more "deeply" encoded semantic features (cf. Craik 8 Lockhart , 1972). (A few theorists assume that visual images may also be used as an alternative...semantically rather than phonemically or graphemically ( Craik & Lockhart , 1972). It is this familiar type of declarative memory of which we are usually
Semantic Webbing, Semantic-Pictorial Webbing and Standard Basal Teaching Techniques: A Comparison of Three Strategies To Enhance Learning and Memory of a Reading Comprehension Task in the Fourth Grade Classroom.

ERIC Educational Resources Information Center

McCarthy-Tucker, Sherri

A study analyzed the relative effectiveness of three teaching strategies for enhancing vocabulary and reading comprehension. Sixty-eight students in three fourth-grade classrooms in a suburban southwestern public school were presented with a vocabulary lesson on weather from the reading text according to one of the following strategies: (1) basal…
Learning Shape Descriptions: Generating and Generalizing Models of Visual Objects.

DTIC Science & Technology

1985-09-01

Minsky , Marvin and Seymour Papert, [1969], Perceptrons, MIT Press, Cam- bridge, Ma. Mitchell, T. M., [1978], "Version spaces: A candidiate elimination...with respect to a suitable set of affine transformations. This is one area in which classic perceptrons fall short [ Minsky and Papert 19691. The third...Quillian, M. Ross, [1968], "Semantic Memory" (PhD Thesis), in Semantic Infor- mation Processing, M. Minsky (ed.), MIT Press, Cambridge MA. Schlesinger, G
Knowledge-based personalized search engine for the Web-based Human Musculoskeletal System Resources (HMSR) in biomechanics.

PubMed

Dao, Tien Tuan; Hoang, Tuan Nha; Ta, Xuan Hien; Tho, Marie Christine Ho Ba

2013-02-01

Human musculoskeletal system resources of the human body are valuable for the learning and medical purposes. Internet-based information from conventional search engines such as Google or Yahoo cannot response to the need of useful, accurate, reliable and good-quality human musculoskeletal resources related to medical processes, pathological knowledge and practical expertise. In this present work, an advanced knowledge-based personalized search engine was developed. Our search engine was based on a client-server multi-layer multi-agent architecture and the principle of semantic web services to acquire dynamically accurate and reliable HMSR information by a semantic processing and visualization approach. A security-enhanced mechanism was applied to protect the medical information. A multi-agent crawler was implemented to develop a content-based database of HMSR information. A new semantic-based PageRank score with related mathematical formulas were also defined and implemented. As the results, semantic web service descriptions were presented in OWL, WSDL and OWL-S formats. Operational scenarios with related web-based interfaces for personal computers and mobile devices were presented and analyzed. Functional comparison between our knowledge-based search engine, a conventional search engine and a semantic search engine showed the originality and the robustness of our knowledge-based personalized search engine. In fact, our knowledge-based personalized search engine allows different users such as orthopedic patient and experts or healthcare system managers or medical students to access remotely into useful, accurate, reliable and good-quality HMSR information for their learning and medical purposes. Copyright © 2012 Elsevier Inc. All rights reserved.
Cooking "shrimp à la créole": a pilot study of an ecological rehabilitation in semantic dementia.

PubMed

Bier, Nathalie; Macoir, Joël; Joubert, Sven; Bottari, Carolina; Chayer, Céline; Pigot, Hélène; Giroux, Sylvain

2011-08-01

New learning in semantic dementia (SD) seems to be tied to a specific temporal and spatial context. Thus, cognitive rehabilitation could capitalise upon preserved episodic memory and focus on everyday activities which, once learned, will have an impact in everyday life. This pilot study thus explores the effectiveness of an ecological approach in one patient suffering from SD. EC, a 68-year-old woman with SD, stopped cooking complex meals due to a substantial loss of knowledge related to all food types. The therapy consisted of preparing a target recipe. She was asked to generate semantic attributes of ingredients found in one target, one control and two no-therapy recipes. The number of recipes cooked by EC between therapy sessions was computed. She was also asked to prepare a generalisation recipe combining ingredients from the target and control recipes. EC's generated semantic attributes (GSA) of ingredients pertaining to the target and control recipes increased significantly (p < .001), compared to the no-therapy recipes (ps > .79). The proportion of meals cooked also increased significantly (p = .021). For the generalisation recipe, she could not succeed without assistance. Frequent food preparation may have provided EC with new memories about the context, usage and appearance of some concepts. These memories seem very context-bound, but EC nonetheless re-introduced some recipes into her day-to-day life. The impact of these results on the relationship between semantic, episodic and procedural memory is discussed, as well as the relevance of an ecological approach in SD.
La perception des morphemes grammaticaux chez les aphasiques (The Perception of Grammatical Morphemes in Aphasics). Montreal Working Papers in Linguistics, Vol. 2.

ERIC Educational Resources Information Center

Goodenough, Cheryl; And Others

Studies have indicated that agrammatical aphasics tend to better realize morphemes with a high level of semantic value. A study sought to examine the effect of the variation of the information content of the article on its comprehension by the aphasic. The appropriate and the significant nature of the function words "the" and "a" were varied with…
Learning to Predict Consequences as a Method of Knowledge Transfer in Reinforcement Learning.

PubMed

Chalmers, Eric; Contreras, Edgar Bermudez; Robertson, Brandon; Luczak, Artur; Gruber, Aaron

2017-04-17

The reinforcement learning (RL) paradigm allows agents to solve tasks through trial-and-error learning. To be capable of efficient, long-term learning, RL agents should be able to apply knowledge gained in the past to new tasks they may encounter in the future. The ability to predict actions' consequences may facilitate such knowledge transfer. We consider here domains where an RL agent has access to two kinds of information: agent-centric information with constant semantics across tasks, and environment-centric information, which is necessary to solve the task, but with semantics that differ between tasks. For example, in robot navigation, environment-centric information may include the robot's geographic location, while agent-centric information may include sensor readings of various nearby obstacles. We propose that these situations provide an opportunity for a very natural style of knowledge transfer, in which the agent learns to predict actions' environmental consequences using agent-centric information. These predictions contain important information about the affordances and dangers present in a novel environment, and can effectively transfer knowledge from agent-centric to environment-centric learning systems. Using several example problems including spatial navigation and network routing, we show that our knowledge transfer approach can allow faster and lower cost learning than existing alternatives.
Reciprocal Effects of Self-Regulation, Semantic Knowledge, and Reading Comprehension in Early Elementary School.

PubMed

Connor, Carol McDonald; Day, Stephanie L; Phillips, Beth; Sparapani, Nicole; Ingebrand, Sarah W; McLean, Leigh; Barrus, Angela; Kaschak, Michael P

2016-11-01

Many assume that cognitive and linguistic processes, such as semantic knowledge (SK) and self-regulation (SR), subserve learned skills like reading. However, complex models of interacting and bootstrapping effects of SK, SR, instruction, and reading hypothesize reciprocal effects. Testing this "lattice" model with children (n = 852) followed from first to second grade (5.9-10.4 years of age) revealed reciprocal effects for reading and SR, and reading and SK, but not SR and SK. More effective literacy instruction reduced reading stability over time. Findings elucidate the synergistic and reciprocal effects of learning to read on other important linguistic, self-regulatory, and cognitive processes; the value of using complex models of development to inform intervention design; and how learned skills may influence development during middle childhood. © 2016 The Authors. Child Development © 2016 Society for Research in Child Development, Inc.
Learning to Read Words in a New Language Shapes the Neural Organization of the Prior Languages

PubMed Central

Mei, Leilei; Xue, Gui; Lu, Zhong-Lin; Chen, Chuansheng; Zhang, Mingxia; He, Qinghua; Wei, Miao; Dong, Qi

2014-01-01

Learning a new language entails interactions with one's prior language(s). Much research has shown how native language affects the cognitive and neural mechanisms of a new language, but little is known about whether and how learning a new language shapes the neural mechanisms of prior language(s). In two experiments in the current study, we used an artificial language training paradigm in combination with fMRI to examine (1) the effects of different linguistic components (phonology and semantics) of a new language on the neural process of prior languages (i.e., native and second languages), and (2) whether such effects were modulated by the proficiency level in the new language. Results of Experiment 1 showed that when the training in a new language involved semantics (as opposed to only visual forms and phonology), neural activity during word reading in the native language (Chinese) was reduced in several reading-related regions, including the left pars opercularis, pars triangularis, bilateral inferior temporal gyrus, fusiform gyrus, and inferior occipital gyrus. Results of Experiment 2 replicated the results of Experiment 1 and further found that semantic training also affected neural activity during word reading in the subjects’ second language (English). Furthermore, we found that the effects of the new language were modulated by the subjects’ proficiency level in the new language. These results provide critical imaging evidence for the influence of learning to read words in a new language on word reading in native and second languages. PMID:25447375
From specific examples to general knowledge in language learning.

PubMed

Tamminen, Jakke; Davis, Matthew H; Rastle, Kathleen

2015-06-01

The extraction of general knowledge from individual episodes is critical if we are to learn new knowledge or abilities. Here we uncover some of the key cognitive mechanisms that characterise this process in the domain of language learning. In five experiments adult participants learned new morphological units embedded in fictitious words created by attaching new affixes (e.g., -afe) to familiar word stems (e.g., "sleepafe is a participant in a study about the effects of sleep"). Participants' ability to generalise semantic knowledge about the affixes was tested using tasks requiring the comprehension and production of novel words containing a trained affix (e.g., sailafe). We manipulated the delay between training and test (Experiment 1), the number of unique exemplars provided for each affix during training (Experiment 2), and the consistency of the form-to-meaning mapping of the affixes (Experiments 3-5). In a task where speeded online language processing is required (semantic priming), generalisation was achieved only after a memory consolidation opportunity following training, and only if the training included a sufficient number of unique exemplars. Semantic inconsistency disrupted speeded generalisation unless consolidation was allowed to operate on one of the two affix-meanings before introducing inconsistencies. In contrast, in tasks that required slow, deliberate reasoning, generalisation could be achieved largely irrespective of the above constraints. These findings point to two different mechanisms of generalisation that have different cognitive demands and rely on different types of memory representations. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.
Automating Expertise in Collaborative Learning Environments

ERIC Educational Resources Information Center

LaVoie, Noelle; Streeter, Lynn; Lochbaum, Karen; Wroblewski, David; Boyce, Lisa; Krupnick, Charles; Psotka, Joseph

2010-01-01

We have developed a set of tools for improving online collaborative learning including an automated expert that monitors and moderates discussions, and additional tools to evaluate contributions, semantically search all posted comments, access a library of hundreds of digital books and provide reports to instructors. The technology behind these…
Semantically-based priors and nuanced knowledge core for Big Data, Social AI, and language understanding.

PubMed

Olsher, Daniel

2014-10-01

Noise-resistant and nuanced, COGBASE makes 10 million pieces of commonsense data and a host of novel reasoning algorithms available via a family of semantically-driven prior probability distributions. Machine learning, Big Data, natural language understanding/processing, and social AI can draw on COGBASE to determine lexical semantics, infer goals and interests, simulate emotion and affect, calculate document gists and topic models, and link commonsense knowledge to domain models and social, spatial, cultural, and psychological data. COGBASE is especially ideal for social Big Data, which tends to involve highly implicit contexts, cognitive artifacts, difficult-to-parse texts, and deep domain knowledge dependencies. Copyright © 2014 Elsevier Ltd. All rights reserved.
When semantics aids phonology: A processing advantage for iconic word forms in aphasia.

PubMed

Meteyard, Lotte; Stoppard, Emily; Snudden, Dee; Cappa, Stefano F; Vigliocco, Gabriella

2015-09-01

Iconicity is the non-arbitrary relation between properties of a phonological form and semantic content (e.g. "moo", "splash"). It is a common feature of both spoken and signed languages, and recent evidence shows that iconic forms confer an advantage during word learning. We explored whether iconic forms conferred a processing advantage for 13 individuals with aphasia following left-hemisphere stroke. Iconic and control words were compared in four different tasks: repetition, reading aloud, auditory lexical decision and visual lexical decision. An advantage for iconic words was seen for some individuals in all tasks, with consistent group effects emerging in reading aloud and auditory lexical decision. Both these tasks rely on mapping between semantics and phonology. We conclude that iconicity aids spoken word processing for individuals with aphasia. This advantage is due to a stronger connection between semantic information and phonological forms. Copyright © 2015 Elsevier Ltd. All rights reserved.
Episodic and semantic memory in posthypnotic amnesia: A reevaluation.

PubMed

Spanos, N P; Radtke, H L; Dubreuil, D L

1982-09-01

Recently, Kihlstrom found that a suggestion for posthypnotic amnesia produced impairments on episodic but not semantic memory tasks. During amnesia testing, highly and very highly susceptible subjects showed reduced recall for a previously learned word list but no deficits on a word association task designed to elicit the forgotten words as associates. He hypnotized that posthypnotic amnesia involved a dissociation between episodic and semantic components of memory. We tested the alternative hypothesis that Kihlstrom's findings resulted from experimental demands conveyed by the wording of the amnesia suggestion he employed. We found that subjects could be induced to show only episodic impairments (thereby replicating Kihlstrom) or both episodic and semantic impairments (contrary to Kihlstrom) by subtly varying the wording of amnesia suggestions. These findings are inconsistent with a dissociation hypothesis. Instead, they support the notion that hypnotic amnesia is a strategic enactment strongly influenced by expectations generated in the amnesia testing situation.
Not all triads are created equal: further support for the importance of visual and semantic proximity in object identification.

PubMed

Schweizer, Tom A; Dixon, Mike J; Desmarais, Geneviève; Smith, Stephen D

2002-01-01

Identification deficits were investigated in ELM, a temporal lobe stroke patient with category-specific deficits. We replicated previous work done on FS, a patient with category specific deficits as a result of herpes viral encephalitis. ELM was tested using novel, computer generated shapes that were paired with artifact labels. We paired semantically close or disparate labels to shapes and ELM attempted to learn these pairings. Overall, ELM's shape-label confusions were most detrimentally affected when we used labels that referred to objects that were visually and semantically close. However, as with FS, ELM had as many errors when shapes were paired with the labels "donut," "tire," and "washer" as he did when they were paired with visually and semantically close artifact labels. Two explanations are put forth to account for the anomalous performance by both patients on the triad of donut-tire-washer.
Learning to Read and Write Polysyllabic Words: The Effects of Morphology and Context on the Acquisition of Whole-Word Representations in Fourth and Fifth Grade

ERIC Educational Resources Information Center

Al Ghanem, Reem

2017-01-01

Accurate and rapid word recognition requires highly-specified phonological, orthographic, and semantic word-specific representations. It has been established that children acquire these representations through phonological decoding in a process known as orthographic learning. Studies examining orthographic learning and its predictors have thus far…
Semantic Features of Math Problems: Relationships to Student Learning and Engagement

ERIC Educational Resources Information Center

Slater, Stefan; Baker, Ryan; Ocumpaugh, Jaclyn; Inventado, Paul; Scupelli, Peter; Heffernan, Neil

2016-01-01

The creation of crowd-sourced content in learning systems is a powerful method for adapting learning systems to the needs of a range of teachers in a range of domains, but the quality of this content can vary. This study explores linguistic differences in teacher-created problem content in ASSISTments using a combination of discovery with models…
It takes biking to learn: Physical activity improves learning a second language.

PubMed

Liu, Fengqin; Sulpizio, Simone; Kornpetpanee, Suchada; Job, Remo

2017-01-01

Recent studies have shown that concurrent physical activity enhances learning a completely unfamiliar L2 vocabulary as compared to learning it in a static condition. In this paper we report a study whose aim is twofold: to test for possible positive effects of physical activity when L2 learning has already reached some level of proficiency, and to test whether the assumed better performance when engaged in physical activity is limited to the linguistic level probed at training (i.e. L2 vocabulary tested by means of a Word-Picture Verification task), or whether it extends also to the sentence level (which was tested by means of a Sentence Semantic Judgment Task). The results show that Chinese speakers with basic knowledge of English benefited from physical activity while learning a set of new words. Furthermore, their better performance emerged also at the sentential level, as shown by their performance in a Semantic Judgment task. Finally, an interesting temporal asymmetry between the lexical and the sentential level emerges, with the difference between the experimental and control group emerging from the 1st testing session at the lexical level but after several weeks at the sentential level.
Game-powered machine learning

PubMed Central

Barrington, Luke; Turnbull, Douglas; Lanckriet, Gert

2012-01-01

Searching for relevant content in a massive amount of multimedia information is facilitated by accurately annotating each image, video, or song with a large number of relevant semantic keywords, or tags. We introduce game-powered machine learning, an integrated approach to annotating multimedia content that combines the effectiveness of human computation, through online games, with the scalability of machine learning. We investigate this framework for labeling music. First, a socially-oriented music annotation game called Herd It collects reliable music annotations based on the “wisdom of the crowds.” Second, these annotated examples are used to train a supervised machine learning system. Third, the machine learning system actively directs the annotation games to collect new data that will most benefit future model iterations. Once trained, the system can automatically annotate a corpus of music much larger than what could be labeled using human computation alone. Automatically annotated songs can be retrieved based on their semantic relevance to text-based queries (e.g., “funky jazz with saxophone,” “spooky electronica,” etc.). Based on the results presented in this paper, we find that actively coupling annotation games with machine learning provides a reliable and scalable approach to making searchable massive amounts of multimedia data. PMID:22460786
Game-powered machine learning.

PubMed

Barrington, Luke; Turnbull, Douglas; Lanckriet, Gert

2012-04-24

Searching for relevant content in a massive amount of multimedia information is facilitated by accurately annotating each image, video, or song with a large number of relevant semantic keywords, or tags. We introduce game-powered machine learning, an integrated approach to annotating multimedia content that combines the effectiveness of human computation, through online games, with the scalability of machine learning. We investigate this framework for labeling music. First, a socially-oriented music annotation game called Herd It collects reliable music annotations based on the "wisdom of the crowds." Second, these annotated examples are used to train a supervised machine learning system. Third, the machine learning system actively directs the annotation games to collect new data that will most benefit future model iterations. Once trained, the system can automatically annotate a corpus of music much larger than what could be labeled using human computation alone. Automatically annotated songs can be retrieved based on their semantic relevance to text-based queries (e.g., "funky jazz with saxophone," "spooky electronica," etc.). Based on the results presented in this paper, we find that actively coupling annotation games with machine learning provides a reliable and scalable approach to making searchable massive amounts of multimedia data.

It takes biking to learn: Physical activity improves learning a second language.

PubMed Central

Liu, Fengqin; Sulpizio, Simone; Kornpetpanee, Suchada; Job, Remo

2017-01-01

Recent studies have shown that concurrent physical activity enhances learning a completely unfamiliar L2 vocabulary as compared to learning it in a static condition. In this paper we report a study whose aim is twofold: to test for possible positive effects of physical activity when L2 learning has already reached some level of proficiency, and to test whether the assumed better performance when engaged in physical activity is limited to the linguistic level probed at training (i.e. L2 vocabulary tested by means of a Word-Picture Verification task), or whether it extends also to the sentence level (which was tested by means of a Sentence Semantic Judgment Task). The results show that Chinese speakers with basic knowledge of English benefited from physical activity while learning a set of new words. Furthermore, their better performance emerged also at the sentential level, as shown by their performance in a Semantic Judgment task. Finally, an interesting temporal asymmetry between the lexical and the sentential level emerges, with the difference between the experimental and control group emerging from the 1st testing session at the lexical level but after several weeks at the sentential level. PMID:28542333
Unsupervised active learning based on hierarchical graph-theoretic clustering.

PubMed

Hu, Weiming; Hu, Wei; Xie, Nianhua; Maybank, Steve

2009-10-01

Most existing active learning approaches are supervised. Supervised active learning has the following problems: inefficiency in dealing with the semantic gap between the distribution of samples in the feature space and their labels, lack of ability in selecting new samples that belong to new categories that have not yet appeared in the training samples, and lack of adaptability to changes in the semantic interpretation of sample categories. To tackle these problems, we propose an unsupervised active learning framework based on hierarchical graph-theoretic clustering. In the framework, two promising graph-theoretic clustering algorithms, namely, dominant-set clustering and spectral clustering, are combined in a hierarchical fashion. Our framework has some advantages, such as ease of implementation, flexibility in architecture, and adaptability to changes in the labeling. Evaluations on data sets for network intrusion detection, image classification, and video classification have demonstrated that our active learning framework can effectively reduce the workload of manual classification while maintaining a high accuracy of automatic classification. It is shown that, overall, our framework outperforms the support-vector-machine-based supervised active learning, particularly in terms of dealing much more efficiently with new samples whose categories have not yet appeared in the training samples.
Event-related brain potentials in memory: correlates of episodic, semantic and implicit memory.

PubMed

Wieser, Stephan; Wieser, Heinz Gregor

2003-06-01

To study cognitive evoked potentials, recorded from scalp EEG and foramen ovale electrodes, during activation of explicit and implicit memory. The subgroups of explicit memory, episodic and semantic memory, are looked at separately. A word-learning task was used, which has been shown to activate hippocampus in H(2)(15)O positron emission tomography studies. Subjects had to study and remember word pairs using different learning strategies: (i) associative word learning (AWL), which activates the episodic memory, (ii) deep single word encoding (DSWE), which activates the semantic memory, and (iii) shallow single word encoding (SSWE), which activates the implicit memory and serves as a baseline. The test included the 'remember/know' paradigm as a behavioural learning control. During the task condition, a 10-20 scalp EEG with additional electrodes in both temporal lobes regions was recorded from 11 healthy volunteers. In one patient with mesiotemporal lobe epilepsy, the EEG was recorded from bilateral foramen ovale electrodes directly from mesial temporal lobe structures. Event-related potentials (ERPs) were calculated off-line and visual and statistical analyses were made. Associative learning strategy produced the best memory performance and the best noetic awareness experience, whereas shallow single word encoding produced the worst performance and the smallest noetic awareness. Deep single word encoding performance was in between. ERPs differed according to the test condition, during both encoding and retrieval, from both the scalp EEG and the foramen ovale electrode recordings. Encoding showed significant differences between the shallow single word encoding (SSWE), which is mainly a function of graphical characteristics, and the other two strategies, deep single word (DSWE) and associative learning (AWL), in which there is a semantic processing of the meaning. ERPs generated by these two categories, which are both functions of explicit memory, differed as well, indicating the presence or the absence of associative binding. Retrieval showed a significant test effect between the word pairs learned by association (AWL) and the ones learned by encoding the words in isolation of each other (DSWE and SSWE). The comparison of the ERPs generated by autonoetic awareness ('remember') and noetic awareness ('know') exhibited a significant test effect as well. The results of behavioural data, in particular that of the 'remember/know' procedure, are evidence that the task paradigm was efficient in activating different kinds of memory. Associative word learning generated a high degree of autonoetic awareness, which is a result of the episodic memory, whereas both kinds of single word learning generated less. AWL, DSWE and SSWE resulted in different electrophysiological correlates, both for encoding as well as retrieval, indicating that different brain structures were activated in different temporal sequence.
Ontology modularization to improve semantic medical image annotation.

PubMed

Wennerberg, Pinar; Schulz, Klaus; Buitelaar, Paul

2011-02-01

Searching for medical images and patient reports is a significant challenge in a clinical setting. The contents of such documents are often not described in sufficient detail thus making it difficult to utilize the inherent wealth of information contained within them. Semantic image annotation addresses this problem by describing the contents of images and reports using medical ontologies. Medical images and patient reports are then linked to each other through common annotations. Subsequently, search algorithms can more effectively find related sets of documents on the basis of these semantic descriptions. A prerequisite to realizing such a semantic search engine is that the data contained within should have been previously annotated with concepts from medical ontologies. One major challenge in this regard is the size and complexity of medical ontologies as annotation sources. Manual annotation is particularly time consuming labor intensive in a clinical environment. In this article we propose an approach to reducing the size of clinical ontologies for more efficient manual image and text annotation. More precisely, our goal is to identify smaller fragments of a large anatomy ontology that are relevant for annotating medical images from patients suffering from lymphoma. Our work is in the area of ontology modularization, which is a recent and active field of research. We describe our approach, methods and data set in detail and we discuss our results. Copyright © 2010 Elsevier Inc. All rights reserved.
A User-Centric Knowledge Creation Model in a Web of Object-Enabled Internet of Things Environment

PubMed Central

Kibria, Muhammad Golam; Fattah, Sheik Mohammad Mostakim; Jeong, Kwanghyeon; Chong, Ilyoung; Jeong, Youn-Kwae

2015-01-01

User-centric service features in a Web of Object-enabled Internet of Things environment can be provided by using a semantic ontology that classifies and integrates objects on the World Wide Web as well as shares and merges context-aware information and accumulated knowledge. The semantic ontology is applied on a Web of Object platform to virtualize the real world physical devices and information to form virtual objects that represent the features and capabilities of devices in the virtual world. Detailed information and functionalities of multiple virtual objects are combined with service rules to form composite virtual objects that offer context-aware knowledge-based services, where context awareness plays an important role in enabling automatic modification of the system to reconfigure the services based on the context. Converting the raw data into meaningful information and connecting the information to form the knowledge and storing and reusing the objects in the knowledge base can both be expressed by semantic ontology. In this paper, a knowledge creation model that synchronizes a service logistic model and a virtual world knowledge model on a Web of Object platform has been proposed. To realize the context-aware knowledge-based service creation and execution, a conceptual semantic ontology model has been developed and a prototype has been implemented for a use case scenario of emergency service. PMID:26393609
A User-Centric Knowledge Creation Model in a Web of Object-Enabled Internet of Things Environment.

PubMed

Kibria, Muhammad Golam; Fattah, Sheik Mohammad Mostakim; Jeong, Kwanghyeon; Chong, Ilyoung; Jeong, Youn-Kwae

2015-09-18

User-centric service features in a Web of Object-enabled Internet of Things environment can be provided by using a semantic ontology that classifies and integrates objects on the World Wide Web as well as shares and merges context-aware information and accumulated knowledge. The semantic ontology is applied on a Web of Object platform to virtualize the real world physical devices and information to form virtual objects that represent the features and capabilities of devices in the virtual world. Detailed information and functionalities of multiple virtual objects are combined with service rules to form composite virtual objects that offer context-aware knowledge-based services, where context awareness plays an important role in enabling automatic modification of the system to reconfigure the services based on the context. Converting the raw data into meaningful information and connecting the information to form the knowledge and storing and reusing the objects in the knowledge base can both be expressed by semantic ontology. In this paper, a knowledge creation model that synchronizes a service logistic model and a virtual world knowledge model on a Web of Object platform has been proposed. To realize the context-aware knowledge-based service creation and execution, a conceptual semantic ontology model has been developed and a prototype has been implemented for a use case scenario of emergency service.
DebugIT for patient safety - improving the treatment with antibiotics through multimedia data mining of heterogeneous clinical data.

PubMed

Lovis, Christian; Colaert, Dirk; Stroetmann, Veli N

2008-01-01

The concepts and architecture underlying a large-scale integrating project funded within the 7th EU Framework Programme (FP7) are discussed. The main objective of the project is to build a tool that will have a significant impact for the monitoring and the control of infectious diseases and antimicrobial resistances in Europe; This will be realized by building a technical and semantic infrastructure able to share heterogeneous clinical data sets from different hospitals in different countries, with different languages and legislations; to analyze large amounts of this clinical data with advanced multimedia data mining and finally apply the obtained knowledge for clinical decisions and outcome monitoring. There are numerous challenges in this project at all levels, technical, semantical, legal and ethical that will have to be addressed.
Depth of Processing and Age Differences.

PubMed

Kheirzadeh, Shiela; Pakzadian, Sarah Sadat

2016-10-01

The present article is aimed to investigate whether there are any differences between youngsters and adults in their working and long-term memory functioning. The theory of Depth of Processing (Craik and Lockhart in J Verbal Learning Verbal Behav 11:671-684, 1972) discusses the varying degrees of strengths of memory traces as the result of differential levels of processing on the retrieved input. Additionally, they claim that there are three levels of visual, auditory and semantic processes applied on the stimuli in the short-term memory leading to discrepancy in the durability of the memory traces and the later ease of recall and retrieval. In the present article, it is tried to demonstrate if there are evidences of more durable memory traces formed after semantic, visual and auditory processions of the incoming language data in two groups of (a) children in their language learning critical age and (b) youngsters who have passed the critical age period. The comparisons of the results made using two-way ANOVAs revealed the superiority of semantic processing for both age groups in recall, retention and consequently recognition of the new English vocabularies by EFL learners.
A Semi-Automatic Approach to Construct Vietnamese Ontology from Online Text

ERIC Educational Resources Information Center

Nguyen, Bao-An; Yang, Don-Lin

2012-01-01

An ontology is an effective formal representation of knowledge used commonly in artificial intelligence, semantic web, software engineering, and information retrieval. In open and distance learning, ontologies are used as knowledge bases for e-learning supplements, educational recommenders, and question answering systems that support students with…
Enactive Metaphors: Learning through Full-Body Engagement

ERIC Educational Resources Information Center

Gallagher, Shaun; Lindgren, Robb

2015-01-01

Building on both cognitive semantics and enactivist approaches to cognition, we explore the concept of enactive metaphor and its implications for learning. Enactive approaches to cognition involve the idea that online sensory-motor and affective processes shape the way the perceiver-thinker experiences the world and interacts with others.…
Vocabulary Learning: Sentence Contexts Linked by Events in Scenarios Facilitate Third Graders' Memory for Verb Meanings

ERIC Educational Resources Information Center

Chilton, Molly Welsh; Ehri, Linnea C.

2015-01-01

An experiment compared the impact of more and less semantically connected sentence contexts on vocabulary learning. Third graders (N = 40) were taught the definitions and meanings of six unfamiliar verbs: "anticipate," "attain," "devise," "restrain," "wield," and "persist." The verbs were…
The Semantization of Vocabulary in Foreign Language Learning.

ERIC Educational Resources Information Center

Beheydt, Ludo

1987-01-01

What is notably missing in the teaching of foreign language vocabulary is a systematically elaborated strategy for vocabulary acquisition that is based on the findings of linguistics and learning psychology. The practical implications of such a double anchorage in linguistics and psychology are outlined in a proposed model of a semantization…
Toward a Semantic Forum for Active Collaborative Learning

ERIC Educational Resources Information Center

Li, Yanyan; Dong, Mingkai; Huang, Ronghuai

2009-01-01

Online discussion forums provide open workspace allowing learners to share information, exchange ideas, address problems and discuss on specific themes. But the substantial impediment to its promotion as effective e-learning facility lies in the continuously increasing messages but with discrete and incoherent structure as well as the loosely-tied…
Indexing Learning Objects: Vocabularies and Empirical Investigation of Consistency

ERIC Educational Resources Information Center

Kabel, Suzanne; De Hoog, Robert; Wielinga, Bob; Anjewierden, Anjo

2004-01-01

In addition to the LOM standard and instructional design specifications, as well as domain specific indexing vocabularies, a structured indexing vocabulary for the more elementary learning objects is advisable in order to support retrieval tasks of developers. Furthermore, because semantic indexing is seen as a difficult task, three issues…
Topic Centred Subject/Language Learning Materials and Their Use.

ERIC Educational Resources Information Center

Rado, Marta

Social bilinguals (second language learners in a linguistic minority) and cultural bilinguals (foreign language learners) often learn side by side in the same classroom, making syllabus planning and choice of teaching methodology difficult. Adopting a bilingual approach and exploring the semantic and discourse aspects of language can overcome the…
Experiential Learning about the Elderly: The Geriatric Medication Game.

ERIC Educational Resources Information Center

Oliver, Carol H.; And Others

1995-01-01

An active learning simulation game designed to increase pharmacy students' awareness of the physical, psychological, and financial difficulties of the ambulatory elderly in handling their medication is described. Questionnaires before and after the game, including a semantic differential tool, indicate that the program is successful in increasing…
Semantic Annotation of Ubiquitous Learning Environments

ERIC Educational Resources Information Center

Weal, M. J.; Michaelides, D. T.; Page, K.; De Roure, D. C.; Monger, E.; Gobbi, M.

2012-01-01

Skills-based learning environments are used to promote the acquisition of practical skills as well as decision making, communication, and problem solving. It is important to provide feedback to the students from these sessions and observations of their actions may inform the assessment process and help researchers to better understand the learning…
Development to Learning: Semantic Shifts in Professional Autonomy and School Leadership

ERIC Educational Resources Information Center

Stevenson, Michael; Hedberg, John G.; O'Sullivan, Kerry-Ann; Howe, Cathie

2015-01-01

In the digital age, technology is playing an important role in changing the nature of professionalism. Newer forms of "professional learning" stand in contrast to more traditional forms of "professional development." The shifting paradigm has implications for school leaders in all contexts. This study sought to qualitatively…
Atypical performance patterns on Delis-Kaplan Executive Functioning System Color-Word Interference Test: Cognitive switching and learning ability in older adults.

PubMed

Berg, Jody-Lynn; Swan, Natasha M; Banks, Sarah J; Miller, Justin B

2016-09-01

Cognitive set shifting requires flexible application of lower level processes. The Delis-Kaplan Executive Functioning System (DKEFS) Color-Word Interference Test (CWIT) is commonly used to clinically assess cognitive set shifting. An atypical pattern of performance has been observed on the CWIT; a subset of individuals perform faster, with equal or fewer errors, on the more difficult inhibition/switching than the inhibition trial. This study seeks to explore the cognitive underpinnings of this atypical pattern. It is hypothesized that atypical patterns on CWIT will be associated with better performance on underlying cognitive measures of attention, working memory, and learning when compared to typical CWIT patterns. Records from 239 clinical referrals (age: M = 68.09 years, SD = 10.62; education: M = 14.87 years, SD = 2.73) seen for a neuropsychological evaluation as part of diagnostic work up in an outpatient dementia and movement disorders clinic were sampled. The standard battery of tests included measures of attention, learning, fluency, executive functioning, and working memory. Analyses of variance (ANOVAs) were conducted to compare the cognitive performance of those with typical versus atypical CWIT patterns. An atypical pattern of performance was confirmed in 23% of our sample. Analyses revealed a significant group difference in acquisition of information on both nonverbal (Brief Visuospatial Memory Test-Revised, BVMT-R total recall), F(1, 213) = 16.61, p < .001, and verbal (Hopkins Verbal Learning Test-Revised, HVLT-R total recall) learning tasks, F(1, 181) = 6.43, p < .01, and semantic fluency (Animal Naming), F(1, 232) = 7.57, p = .006, with the atypical group performing better on each task. Effect sizes were larger for nonverbal (Cohen's d = 0.66) than verbal learning (Cohen's d = 0.47) and semantic fluency (Cohen's d = 0.43). Individuals demonstrating an atypical pattern of performance on the CWIT inhibition/switching trial also demonstrated relative strengths in semantic fluency and learning.
Connectionism and Compositional Semantics

DTIC Science & Technology

1989-05-01

can use their hidden layers to learn difficult discriminations. such as panty or the Penzias two clumps/three clumps problem, where the output is...sauce." For novel sentences that are similar to the training sentences (e.g., train on "the girl hit the boy," test on -the boy hit the girl "), the...overridden by semantic considerations. as in this example from Wendy Lehnert (personal communicanon): (5) John saw the girl with the telescope in a red

Lessons learned in detailed clinical modeling at Intermountain Healthcare

PubMed Central

Oniki, Thomas A; Coyle, Joseph F; Parker, Craig G; Huff, Stanley M

2014-01-01

Background and objective Intermountain Healthcare has a long history of using coded terminology and detailed clinical models (DCMs) to govern storage of clinical data to facilitate decision support and semantic interoperability. The latest iteration of DCMs at Intermountain is called the clinical element model (CEM). We describe the lessons learned from our CEM efforts with regard to subjective decisions a modeler frequently needs to make in creating a CEM. We present insights and guidelines, but also describe situations in which use cases conflict with the guidelines. We propose strategies that can help reconcile the conflicts. The hope is that these lessons will be helpful to others who are developing and maintaining DCMs in order to promote sharing and interoperability. Methods We have used the Clinical Element Modeling Language (CEML) to author approximately 5000 CEMs. Results Based on our experience, we have formulated guidelines to lead our modelers through the subjective decisions they need to make when authoring models. Reported here are guidelines regarding precoordination/postcoordination, dividing content between the model and the terminology, modeling logical attributes, and creating iso-semantic models. We place our lessons in context, exploring the potential benefits of an implementation layer, an iso-semantic modeling framework, and ontologic technologies. Conclusions We assert that detailed clinical models can advance interoperability and sharing, and that our guidelines, an implementation layer, and an iso-semantic framework will support our progress toward that goal. PMID:24993546
Rules and construction effects in learning the argument structure of verbs.

PubMed

Demuth, Katherine; Machobane, Malillo; Moloi, Francina

2003-11-01

Theorists of language acquisition have long debated the means by which children learn the argument structure of verbs (e.g. Bowerman, 1974, 1990; Pinker, 1984, 1989; Tomasello, 1992). Central to this controversy has been the possible role of verb semantics, especially in learning which verbs undergo dative-shift alternation in languages like English. The learning problem is somewhat simplified in Bantu double object constructions, where all applicative verbs show the same order of postverbal objects. However, Bantu languages differ as to what that order is, some placing the benefactive argument first, and others placing the animate argument first. Learning the language-specific word-order restrictions on Bantu double object applicative constructions is therefore more akin to setting a parameter (cf. Hyams, 1986). This study examined 100 three- to eight-year-old children's knowledge of word order restrictions in Sesotho double object applicatives. Performance on forced choice elicited production tasks found that four-year-olds showed evidence of rule learning, although eight-year-olds had not yet attained adult levels of performance. Further investigation found lexical construction effects for three-year-olds. These findings suggest that learning the argument structure of verbs, even when lexical semantics is not involved, may be more sensitive to lexical construction effects than previously thought.
Usable, Real-Time, Interactive Spoken Language Systems

DTIC Science & Technology

1994-09-01

Similarly, we included derivations (mostly plurals and possessives) of many open-class words in the domnain. We also added about 400 concatenated word...UueraiCe’l~ usinig a system of’ ’realization 1111C, %%. hiCh map) thle gr-aimmlatcal relation anl argumlent bears to the head onto thle semantic relatio ...syntactic categories as well. Representations of this form contain significantly more internal structure than specialized sublanguage models. This can be
Surfing the Waves of Learning: Enacting a Semantics Analysis of Teaching in a First-Year Law Course

ERIC Educational Resources Information Center

Clarence, Sherran

2017-01-01

Students' ability to build knowledge, and transfer it within and between contexts is crucial to cumulative learning and to academic success. This has long been a concern of higher education research and practice. A central part of this concern for educators is creating the conditions that enable their students' deep learning, as this is an area of…
Assessing Classroom Learning: How Students Use Their Knowledge and Experience to Answer Classroom Achievement Test Questions in Science and Social Studies.

ERIC Educational Resources Information Center

Nuthall, Graham; Alton-Lee, Adrienne

1995-01-01

Observational studies of student learning from classroom experience in science and social studies in elementary and middle school classrooms were carried out with 14 students. A model is described that explains how students use multilayered episodic and semantic memory for learning experience and related knowledge to answer achievement test items.…
Incidental Learning: A Brief, Valid Measure of Memory Based on the WAIS-IV Vocabulary and Similarities Subtests.

PubMed

Spencer, Robert J; Reckow, Jaclyn; Drag, Lauren L; Bieliauskas, Linas A

2016-12-01

We assessed the validity of a brief incidental learning measure based on the Similarities and Vocabulary subtests of the Wechsler Adult Intelligence Scale-Fourth Edition (WAIS-IV). Most neuropsychological assessments for memory require intentional learning, but incidental learning occurs without explicit instruction. Incidental memory tests such as the WAIS-III Symbol Digit Coding subtest have existed for many years, but few memory studies have used a semantically processed incidental learning model. We conducted a retrospective analysis of 37 veterans with traumatic brain injury, referred for outpatient neuropsychological testing at a Veterans Affairs hospital. As part of their evaluation, the participants completed the incidental learning tasks. We compared their incidental learning performance to their performance on traditional memory measures. Incidental learning scores correlated strongly with scores on the California Verbal Learning Test-Second Edition (CVLT-II) and Brief Visuospatial Memory Test-Revised (BVMT-R). After we conducted a partial correlation that controlled for the effects of age, incidental learning correlated significantly with the CVLT-II Immediate Free Recall, CVLT-II Short-Delay Recall, CVLT-II Long-Delay Recall, and CVLT-II Yes/No Recognition Hits, and with the BVMT-R Delayed Recall and BVMT-R Recognition Discrimination Index. Our incidental learning procedures derived from subtests of the WAIS-IV Edition are an efficient and valid way of measuring memory. These tasks add minimally to testing time and capitalize on the semantic encoding that is inherent in completing the Similarities and Vocabulary subtests.
Newly-acquired words are more phonologically robust in verbal short-term memory when they have associated semantic representations.

PubMed

Savill, Nicola; Ellis, Andrew W; Jefferies, Elizabeth

2017-04-01

Verbal short-term memory (STM) is a crucial cognitive function central to language learning, comprehension and reasoning, yet the processes that underlie this capacity are not fully understood. In particular, although STM primarily draws on a phonological code, interactions between long-term phonological and semantic representations might help to stabilise the phonological trace for words ("semantic binding hypothesis"). This idea was first proposed to explain the frequent phoneme recombination errors made by patients with semantic dementia when recalling words that are no longer fully understood. However, converging evidence in support of semantic binding is scant: it is unusual for studies of healthy participants to examine serial recall at the phoneme level and also it is difficult to separate the contribution of phonological-lexical knowledge from effects of word meaning. We used a new method to disentangle these influences in healthy individuals by training new 'words' with or without associated semantic information. We examined phonological coherence in immediate serial recall (ISR), both immediately and the day after training. Trained items were more likely to be recalled than novel nonwords, confirming the importance of phonological-lexical knowledge, and items with semantic associations were also produced more accurately than those with no meaning, at both time points. For semantically-trained items, there were fewer phoneme ordering and identity errors, and consequently more complete target items were produced in both correct and incorrect list positions. These data show that lexical-semantic knowledge improves the robustness of verbal STM at the sub-item level, even when the effect of phonological familiarity is taken into account. Copyright © 2016 Elsevier Ltd. All rights reserved.
Representation of Semantic Similarity in the Left Intraparietal Sulcus: Functional Magnetic Resonance Imaging Evidence

PubMed Central

Neyens, Veerle; Bruffaerts, Rose; Liuzzi, Antonietta G.; Kalfas, Ioannis; Peeters, Ronald; Keuleers, Emmanuel; Vogels, Rufin; De Deyne, Simon; Storms, Gert; Dupont, Patrick; Vandenberghe, Rik

2017-01-01

According to a recent study, semantic similarity between concrete entities correlates with the similarity of activity patterns in left middle IPS during category naming. We examined the replicability of this effect under passive viewing conditions, the potential role of visuoperceptual similarity, where the effect is situated compared to regions that have been previously implicated in visuospatial attention, and how it compares to effects of object identity and location. Forty-six subjects participated. Subjects passively viewed pictures from two categories, musical instruments and vehicles. Semantic similarity between entities was estimated based on a concept-feature matrix obtained in more than 1,000 subjects. Visuoperceptual similarity was modeled based on the HMAX model, the AlexNet deep convolutional learning model, and thirdly, based on subjective visuoperceptual similarity ratings. Among the IPS regions examined, only left middle IPS showed a semantic similarity effect. The effect was significant in hIP1, hIP2, and hIP3. Visuoperceptual similarity did not correlate with similarity of activity patterns in left middle IPS. The semantic similarity effect in left middle IPS was significantly stronger than in the right middle IPS and also stronger than in the left or right posterior IPS. The semantic similarity effect was similar to that seen in the angular gyrus. Object identity effects were much more widespread across nearly all parietal areas examined. Location effects were relatively specific for posterior IPS and area 7 bilaterally. To conclude, the current findings replicate the semantic similarity effect in left middle IPS under passive viewing conditions, and demonstrate its anatomical specificity within a cytoarchitectonic reference frame. We propose that the semantic similarity effect in left middle IPS reflects the transient uploading of semantic representations in working memory. PMID:28824405
Lexicality Effects in Word and Nonword Recall of Semantic Dementia and Progressive Nonfluent Aphasia

PubMed Central

Reilly, Jamie; Troche, Joshua; Chatel, Alison; Park, Hyejin; Kalinyak-Fliszar, Michelene; Antonucci, Sharon M.; Martin, Nadine

2012-01-01

Background Verbal working memory is an essential component of many language functions, including sentence comprehension and word learning. As such, working memory has emerged as a domain of intense research interest both in aphasiology and in the broader field of cognitive neuroscience. The integrity of verbal working memory encoding relies on a fluid interaction between semantic and phonological processes. That is, we encode verbal detail using many cues related to both the sound and meaning of words. Lesion models can provide an effective means of parsing the contributions of phonological or semantic impairment to recall performance. Methods and Procedures We employed the lesion model approach here by contrasting the nature of lexicality errors incurred during recall of word and nonword sequences by 3individuals with progressive nonfluent aphasia (a phonological dominant impairment) compared to that of 2 individuals with semantic dementia (a semantic dominant impairment). We focused on psycholinguistic attributes of correctly recalled stimuli relative to those that elicited a lexicality error (i.e., nonword → word OR word → nonword). Outcomes and results Patients with semantic dementia showed greater sensitivity to phonological attributes (e.g., phoneme length, wordlikeness) of the target items relative to semantic attributes (e.g., familiarity). Patients with PNFA showed the opposite pattern, marked by sensitivity to word frequency, age of acquisition, familiarity, and imageability. Conclusions We interpret these results in favor of a processing strategy such that in the context of a focal phonological impairment patients revert to an over-reliance on preserved semantic processing abilities. In contrast, a focal semantic impairment forces both reliance upon and hypersensitivity to phonological attributes of target words. We relate this interpretation to previous hypotheses about the nature of verbal short-term memory in progressive aphasia. PMID:23486736
Statistical Learning Is Constrained to Less Abstract Patterns in Complex Sensory Input (but not the Least)

PubMed Central

Emberson, Lauren L.; Rubinstein, Dani

2016-01-01

The influence of statistical information on behavior (either through learning or adaptation) is quickly becoming foundational to many domains of cognitive psychology and cognitive neuroscience, from language comprehension to visual development. We investigate a central problem impacting these diverse fields: when encountering input with rich statistical information, are there any constraints on learning? This paper examines learning outcomes when adult learners are given statistical information across multiple levels of abstraction simultaneously: from abstract, semantic categories of everyday objects to individual viewpoints on these objects. After revealing statistical learning of abstract, semantic categories with scrambled individual exemplars (Exp. 1), participants viewed pictures where the categories as well as the individual objects predicted picture order (e.g., bird1—dog1, bird2—dog2). Our findings suggest that participants preferentially encode the relationships between the individual objects, even in the presence of statistical regularities linking semantic categories (Exps. 2 and 3). In a final experiment we investigate whether learners are biased towards learning object-level regularities or simply construct the most detailed model given the data (and therefore best able to predict the specifics of the upcoming stimulus) by investigating whether participants preferentially learn from the statistical regularities linking individual snapshots of objects or the relationship between the objects themselves (e.g., bird_picture1— dog_picture1, bird_picture2—dog_picture2). We find that participants fail to learn the relationships between individual snapshots, suggesting a bias towards object-level statistical regularities as opposed to merely constructing the most complete model of the input. This work moves beyond the previous existence proofs that statistical learning is possible at both very high and very low levels of abstraction (categories vs. individual objects) and suggests that, at least with the current categories and type of learner, there are biases to pick up on statistical regularities between individual objects even when robust statistical information is present at other levels of abstraction. These findings speak directly to emerging theories about how systems supporting statistical learning and prediction operate in our structure-rich environments. Moreover, the theoretical implications of the current work across multiple domains of study is already clear: statistical learning cannot be assumed to be unconstrained even if statistical learning has previously been established at a given level of abstraction when that information is presented in isolation. PMID:27139779
Semantic Entity-Component State Management Techniques to Enhance Software Quality for Multimodal VR-Systems.

PubMed

Fischbach, Martin; Wiebusch, Dennis; Latoschik, Marc Erich

2017-04-01

Modularity, modifiability, reusability, and API usability are important software qualities that determine the maintainability of software architectures. Virtual, Augmented, and Mixed Reality (VR, AR, MR) systems, modern computer games, as well as interactive human-robot systems often include various dedicated input-, output-, and processing subsystems. These subsystems collectively maintain a real-time simulation of a coherent application state. The resulting interdependencies between individual state representations, mutual state access, overall synchronization, and flow of control implies a conceptual close coupling whereas software quality asks for a decoupling to develop maintainable solutions. This article presents five semantics-based software techniques that address this contradiction: Semantic grounding, code from semantics, grounded actions, semantic queries, and decoupling by semantics. These techniques are applied to extend the well-established entity-component-system (ECS) pattern to overcome some of this pattern's deficits with respect to the implied state access. A walk-through of central implementation aspects of a multimodal (speech and gesture) VR-interface is used to highlight the techniques' benefits. This use-case is chosen as a prototypical example of complex architectures with multiple interacting subsystems found in many VR, AR and MR architectures. Finally, implementation hints are given, lessons learned regarding maintainability pointed-out, and performance implications discussed.
Visual noise disrupts conceptual integration in reading.

PubMed

Gao, Xuefei; Stine-Morrow, Elizabeth A L; Noh, Soo Rim; Eskew, Rhea T

2011-02-01

The Effortfulness Hypothesis suggests that sensory impairment (either simulated or age-related) may decrease capacity for semantic integration in language comprehension. We directly tested this hypothesis by measuring resource allocation to different levels of processing during reading (i.e., word vs. semantic analysis). College students read three sets of passages word-by-word, one at each of three levels of dynamic visual noise. There was a reliable interaction between processing level and noise, such that visual noise increased resources allocated to word-level processing, at the cost of attention paid to semantic analysis. Recall of the most important ideas also decreased with increasing visual noise. Results suggest that sensory challenge can impair higher-level cognitive functions in learning from text, supporting the Effortfulness Hypothesis.
Learning to read words in a new language shapes the neural organization of the prior languages.

PubMed

Mei, Leilei; Xue, Gui; Lu, Zhong-Lin; Chen, Chuansheng; Zhang, Mingxia; He, Qinghua; Wei, Miao; Dong, Qi

2014-12-01

Learning a new language entails interactions with one׳s prior language(s). Much research has shown how native language affects the cognitive and neural mechanisms of a new language, but little is known about whether and how learning a new language shapes the neural mechanisms of prior language(s). In two experiments in the current study, we used an artificial language training paradigm in combination with an fMRI to examine (1) the effects of different linguistic components (phonology and semantics) of a new language on the neural process of prior languages (i.e., native and second languages), and (2) whether such effects were modulated by the proficiency level in the new language. Results of Experiment 1 showed that when the training in a new language involved semantics (as opposed to only visual forms and phonology), neural activity during word reading in the native language (Chinese) was reduced in several reading-related regions, including the left pars opercularis, pars triangularis, bilateral inferior temporal gyrus, fusiform gyrus, and inferior occipital gyrus. Results of Experiment 2 replicated the results of Experiment 1 and further found that semantic training also affected neural activity during word reading in the subjects׳ second language (English). Furthermore, we found that the effects of the new language were modulated by the subjects׳ proficiency level in the new language. These results provide critical imaging evidence for the influence of learning to read words in a new language on word reading in native and second languages. Copyright © 2014 Elsevier Ltd. All rights reserved.
Toward an Episodic Context Account of Retrieval-Based Learning: Dissociating Retrieval Practice and Elaboration

ERIC Educational Resources Information Center

Lehman, Melissa; Smith, Megan A.; Karpicke, Jeffrey D.

2014-01-01

We tested the predictions of 2 explanations for retrieval-based learning; while the elaborative retrieval hypothesis assumes that the retrieval of studied information promotes the generation of semantically related information, which aids in later retrieval (Carpenter, 2009), the episodic context account proposed by Karpicke, Lehman, and Aue (in…
Recasts Used with Preschoolers Learning English as Their Second Language

ERIC Educational Resources Information Center

Tsybina, Irina; Girolametto, Luigi E.; Weitzman, Elaine; Greenberg, Janice

2006-01-01

This study examined linguistic recasts provided by 16 early childhood educators to preschool children learning English as a second language (EL2). Recasts are semantic and syntactic revisions of children's utterances. The educator-child interactions were filmed during book reading and play dough activities with small groups of four children, one…
Information Resources Usage in Project Management Digital Learning System

ERIC Educational Resources Information Center

Davidovitch, Nitza; Belichenko, Margarita; Kravchenko, Yurii

2017-01-01

The article combines a theoretical approach to structuring knowledge that is based on the integrated use of fuzzy semantic network theory predicates, Boolean functions, theory of complexity of network structures and some practical aspects to be considered in the distance learning at the university. The paper proposes a methodological approach that…
A Semantic Approach to Intelligent and Personal Tutoring System

ERIC Educational Resources Information Center

Sette, Maria

2017-01-01

Cyberlearning presents numerous challenges such as the lack of personal and assessment-driven learning, how students are often puzzled by the lack of instructor guidance and feedback, the huge volume of diverse learning materials, and the inability to zoom in from the general concepts to the more specific ones, or vice versa. Intelligent tutoring…
A Chatbot for a Dialogue-Based Second Language Learning System

ERIC Educational Resources Information Center

Huang, Jin-Xia; Lee, Kyung-Soon; Kwon, Oh-Woog; Kim, Young-Kil

2017-01-01

This paper presents a chatbot for a Dialogue-Based Computer-Assisted second Language Learning (DB-CALL) system. A DB-CALL system normally leads dialogues by asking questions according to given scenarios. User utterances outside the scenarios are normally considered as semantically improper and simply rejected. In this paper, we assume that raising…
Exploring the Nature of Disciplinary Teaching and Learning Using Legitimation Code Theory Semantics

ERIC Educational Resources Information Center

Clarence, Sherran

2016-01-01

Teaching and learning is a growing field of research and practice globally, and increasing investments are being made in developing academics as teachers. An inability to adequately account for disciplinary knowledge can lead to academic development inputs that are unable to fully address the needs of students, educators, or disciplines…
Principal Component Analysis Study of Visual and Verbal Metaphoric Comprehension in Children with Autism and Learning Disabilities

ERIC Educational Resources Information Center

Mashal, Nira; Kasirer, Anat

2012-01-01

This research extends previous studies regarding the metaphoric competence of autistic and learning disabled children on different measures of visual and verbal non-literal language comprehension, as well as cognitive abilities that include semantic knowledge, executive functions, similarities, and reading fluency. Thirty seven children with…

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