10 CFR 300.3 - Guidance for defining and naming the reporting entity.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 10 Energy 3 2012-01-01 2012-01-01 false Guidance for defining and naming the reporting entity. 300.3 Section 300.3 Energy DEPARTMENT OF ENERGY CLIMATE CHANGE VOLUNTARY GREENHOUSE GAS REPORTING PROGRAM: GENERAL GUIDELINES § 300.3 Guidance for defining and naming the reporting entity. (a) A reporting...

10 CFR 300.3 - Guidance for defining and naming the reporting entity.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 10 Energy 3 2011-01-01 2011-01-01 false Guidance for defining and naming the reporting entity. 300.3 Section 300.3 Energy DEPARTMENT OF ENERGY CLIMATE CHANGE VOLUNTARY GREENHOUSE GAS REPORTING PROGRAM: GENERAL GUIDELINES § 300.3 Guidance for defining and naming the reporting entity. (a) A reporting...

10 CFR 300.3 - Guidance for defining and naming the reporting entity.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 10 Energy 3 2014-01-01 2014-01-01 false Guidance for defining and naming the reporting entity. 300.3 Section 300.3 Energy DEPARTMENT OF ENERGY CLIMATE CHANGE VOLUNTARY GREENHOUSE GAS REPORTING PROGRAM: GENERAL GUIDELINES § 300.3 Guidance for defining and naming the reporting entity. (a) A reporting...

10 CFR 300.3 - Guidance for defining and naming the reporting entity.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 10 Energy 3 2010-01-01 2010-01-01 false Guidance for defining and naming the reporting entity. 300.3 Section 300.3 Energy DEPARTMENT OF ENERGY CLIMATE CHANGE VOLUNTARY GREENHOUSE GAS REPORTING PROGRAM: GENERAL GUIDELINES § 300.3 Guidance for defining and naming the reporting entity. (a) A reporting...

Medical Named Entity Recognition for Indonesian Language Using Word Representations

NASA Astrophysics Data System (ADS)

Rahman, Arief

2018-03-01

Nowadays, Named Entity Recognition (NER) system is used in medical texts to obtain important medical information, like diseases, symptoms, and drugs. While most NER systems are applied to formal medical texts, informal ones like those from social media (also called semi-formal texts) are starting to get recognition as a gold mine for medical information. We propose a theoretical Named Entity Recognition (NER) model for semi-formal medical texts in our medical knowledge management system by comparing two kinds of word representations: cluster-based word representation and distributed representation.

Leveraging Pattern Semantics for Extracting Entities in Enterprises.

PubMed

Tao, Fangbo; Zhao, Bo; Fuxman, Ariel; Li, Yang; Han, Jiawei

2015-05-01

Entity Extraction is a process of identifying meaningful entities from text documents. In enterprises, extracting entities improves enterprise efficiency by facilitating numerous applications, including search, recommendation, etc. However, the problem is particularly challenging on enterprise domains due to several reasons. First, the lack of redundancy of enterprise entities makes previous web-based systems like NELL and OpenIE not effective, since using only high-precision/low-recall patterns like those systems would miss the majority of sparse enterprise entities, while using more low-precision patterns in sparse setting also introduces noise drastically. Second, semantic drift is common in enterprises ("Blue" refers to "Windows Blue"), such that public signals from the web cannot be directly applied on entities. Moreover, many internal entities never appear on the web. Sparse internal signals are the only source for discovering them. To address these challenges, we propose an end-to-end framework for extracting entities in enterprises, taking the input of enterprise corpus and limited seeds to generate a high-quality entity collection as output. We introduce the novel concept of Semantic Pattern Graph to leverage public signals to understand the underlying semantics of lexical patterns, reinforce pattern evaluation using mined semantics, and yield more accurate and complete entities. Experiments on Microsoft enterprise data show the effectiveness of our approach.

Transfer learning for biomedical named entity recognition with neural networks.

PubMed

Giorgi, John M; Bader, Gary D

2018-06-01

The explosive increase of biomedical literature has made information extraction an increasingly important tool for biomedical research. A fundamental task is the recognition of biomedical named entities in text (BNER) such as genes/proteins, diseases, and species. Recently, a domain-independent method based on deep learning and statistical word embeddings, called long short-term memory network-conditional random field (LSTM-CRF), has been shown to outperform state-of-the-art entity-specific BNER tools. However, this method is dependent on gold-standard corpora (GSCs) consisting of hand-labeled entities, which tend to be small but highly reliable. An alternative to GSCs are silver-standard corpora (SSCs), which are generated by harmonizing the annotations made by several automatic annotation systems. SSCs typically contain more noise than GSCs but have the advantage of containing many more training examples. Ideally, these corpora could be combined to achieve the benefits of both, which is an opportunity for transfer learning. In this work, we analyze to what extent transfer learning improves upon state-of-the-art results for BNER. We demonstrate that transferring a deep neural network (DNN) trained on a large, noisy SSC to a smaller, but more reliable GSC significantly improves upon state-of-the-art results for BNER. Compared to a state-of-the-art baseline evaluated on 23 GSCs covering four different entity classes, transfer learning results in an average reduction in error of approximately 11%. We found transfer learning to be especially beneficial for target data sets with a small number of labels (approximately 6000 or less). Source code for the LSTM-CRF is available athttps://github.com/Franck-Dernoncourt/NeuroNER/ and links to the corpora are available athttps://github.com/BaderLab/Transfer-Learning-BNER-Bioinformatics-2018/. john.giorgi@utoronto.ca. Supplementary data are available at Bioinformatics online.

A study of machine-learning-based approaches to extract clinical entities and their assertions from discharge summaries.

PubMed

Jiang, Min; Chen, Yukun; Liu, Mei; Rosenbloom, S Trent; Mani, Subramani; Denny, Joshua C; Xu, Hua

2011-01-01

The authors' goal was to develop and evaluate machine-learning-based approaches to extracting clinical entities-including medical problems, tests, and treatments, as well as their asserted status-from hospital discharge summaries written using natural language. This project was part of the 2010 Center of Informatics for Integrating Biology and the Bedside/Veterans Affairs (VA) natural-language-processing challenge. The authors implemented a machine-learning-based named entity recognition system for clinical text and systematically evaluated the contributions of different types of features and ML algorithms, using a training corpus of 349 annotated notes. Based on the results from training data, the authors developed a novel hybrid clinical entity extraction system, which integrated heuristic rule-based modules with the ML-base named entity recognition module. The authors applied the hybrid system to the concept extraction and assertion classification tasks in the challenge and evaluated its performance using a test data set with 477 annotated notes. Standard measures including precision, recall, and F-measure were calculated using the evaluation script provided by the Center of Informatics for Integrating Biology and the Bedside/VA challenge organizers. The overall performance for all three types of clinical entities and all six types of assertions across 477 annotated notes were considered as the primary metric in the challenge. Systematic evaluation on the training set showed that Conditional Random Fields outperformed Support Vector Machines, and semantic information from existing natural-language-processing systems largely improved performance, although contributions from different types of features varied. The authors' hybrid entity extraction system achieved a maximum overall F-score of 0.8391 for concept extraction (ranked second) and 0.9313 for assertion classification (ranked fourth, but not statistically different than the first three systems) on the test

Leveraging Pattern Semantics for Extracting Entities in Enterprises

PubMed Central

Tao, Fangbo; Zhao, Bo; Fuxman, Ariel; Li, Yang; Han, Jiawei

2015-01-01

Entity Extraction is a process of identifying meaningful entities from text documents. In enterprises, extracting entities improves enterprise efficiency by facilitating numerous applications, including search, recommendation, etc. However, the problem is particularly challenging on enterprise domains due to several reasons. First, the lack of redundancy of enterprise entities makes previous web-based systems like NELL and OpenIE not effective, since using only high-precision/low-recall patterns like those systems would miss the majority of sparse enterprise entities, while using more low-precision patterns in sparse setting also introduces noise drastically. Second, semantic drift is common in enterprises (“Blue” refers to “Windows Blue”), such that public signals from the web cannot be directly applied on entities. Moreover, many internal entities never appear on the web. Sparse internal signals are the only source for discovering them. To address these challenges, we propose an end-to-end framework for extracting entities in enterprises, taking the input of enterprise corpus and limited seeds to generate a high-quality entity collection as output. We introduce the novel concept of Semantic Pattern Graph to leverage public signals to understand the underlying semantics of lexical patterns, reinforce pattern evaluation using mined semantics, and yield more accurate and complete entities. Experiments on Microsoft enterprise data show the effectiveness of our approach. PMID:26705540

An attention-based BiLSTM-CRF approach to document-level chemical named entity recognition.

PubMed

Luo, Ling; Yang, Zhihao; Yang, Pei; Zhang, Yin; Wang, Lei; Lin, Hongfei; Wang, Jian

2018-04-15

In biomedical research, chemical is an important class of entities, and chemical named entity recognition (NER) is an important task in the field of biomedical information extraction. However, most popular chemical NER methods are based on traditional machine learning and their performances are heavily dependent on the feature engineering. Moreover, these methods are sentence-level ones which have the tagging inconsistency problem. In this paper, we propose a neural network approach, i.e. attention-based bidirectional Long Short-Term Memory with a conditional random field layer (Att-BiLSTM-CRF), to document-level chemical NER. The approach leverages document-level global information obtained by attention mechanism to enforce tagging consistency across multiple instances of the same token in a document. It achieves better performances with little feature engineering than other state-of-the-art methods on the BioCreative IV chemical compound and drug name recognition (CHEMDNER) corpus and the BioCreative V chemical-disease relation (CDR) task corpus (the F-scores of 91.14 and 92.57%, respectively). Data and code are available at https://github.com/lingluodlut/Att-ChemdNER. yangzh@dlut.edu.cn or wangleibihami@gmail.com. Supplementary data are available at Bioinformatics online.

A transition-based joint model for disease named entity recognition and normalization.

PubMed

Lou, Yinxia; Zhang, Yue; Qian, Tao; Li, Fei; Xiong, Shufeng; Ji, Donghong

2017-08-01

Disease named entities play a central role in many areas of biomedical research, and automatic recognition and normalization of such entities have received increasing attention in biomedical research communities. Existing methods typically used pipeline models with two independent phases: (i) a disease named entity recognition (DER) system is used to find the boundaries of mentions in text and (ii) a disease named entity normalization (DEN) system is used to connect the mentions recognized to concepts in a controlled vocabulary. The main problems of such models are: (i) there is error propagation from DER to DEN and (ii) DEN is useful for DER, but pipeline models cannot utilize this. We propose a transition-based model to jointly perform disease named entity recognition and normalization, casting the output construction process into an incremental state transition process, learning sequences of transition actions globally, which correspond to joint structural outputs. Beam search and online structured learning are used, with learning being designed to guide search. Compared with the only existing method for joint DEN and DER, our method allows non-local features to be used, which significantly improves the accuracies. We evaluate our model on two corpora: the BioCreative V Chemical Disease Relation (CDR) corpus and the NCBI disease corpus. Experiments show that our joint framework achieves significantly higher performances compared to competitive pipeline baselines. Our method compares favourably to other state-of-the-art approaches. Data and code are available at https://github.com/louyinxia/jointRN. dhji@whu.edu.cn. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Chemical name extraction based on automatic training data generation and rich feature set.

PubMed

Yan, Su; Spangler, W Scott; Chen, Ying

2013-01-01

The automation of extracting chemical names from text has significant value to biomedical and life science research. A major barrier in this task is the difficulty of getting a sizable and good quality data to train a reliable entity extraction model. Another difficulty is the selection of informative features of chemical names, since comprehensive domain knowledge on chemistry nomenclature is required. Leveraging random text generation techniques, we explore the idea of automatically creating training sets for the task of chemical name extraction. Assuming the availability of an incomplete list of chemical names, called a dictionary, we are able to generate well-controlled, random, yet realistic chemical-like training documents. We statistically analyze the construction of chemical names based on the incomplete dictionary, and propose a series of new features, without relying on any domain knowledge. Compared to state-of-the-art models learned from manually labeled data and domain knowledge, our solution shows better or comparable results in annotating real-world data with less human effort. Moreover, we report an interesting observation about the language for chemical names. That is, both the structural and semantic components of chemical names follow a Zipfian distribution, which resembles many natural languages.

Using Workflows to Explore and Optimise Named Entity Recognition for Chemistry

PubMed Central

Kolluru, BalaKrishna; Hawizy, Lezan; Murray-Rust, Peter; Tsujii, Junichi; Ananiadou, Sophia

2011-01-01

Chemistry text mining tools should be interoperable and adaptable regardless of system-level implementation, installation or even programming issues. We aim to abstract the functionality of these tools from the underlying implementation via reconfigurable workflows for automatically identifying chemical names. To achieve this, we refactored an established named entity recogniser (in the chemistry domain), OSCAR and studied the impact of each component on the net performance. We developed two reconfigurable workflows from OSCAR using an interoperable text mining framework, U-Compare. These workflows can be altered using the drag-&-drop mechanism of the graphical user interface of U-Compare. These workflows also provide a platform to study the relationship between text mining components such as tokenisation and named entity recognition (using maximum entropy Markov model (MEMM) and pattern recognition based classifiers). Results indicate that, for chemistry in particular, eliminating noise generated by tokenisation techniques lead to a slightly better performance than others, in terms of named entity recognition (NER) accuracy. Poor tokenisation translates into poorer input to the classifier components which in turn leads to an increase in Type I or Type II errors, thus, lowering the overall performance. On the Sciborg corpus, the workflow based system, which uses a new tokeniser whilst retaining the same MEMM component, increases the F-score from 82.35% to 84.44%. On the PubMed corpus, it recorded an F-score of 84.84% as against 84.23% by OSCAR. PMID:21633495

Using workflows to explore and optimise named entity recognition for chemistry.

PubMed

Kolluru, Balakrishna; Hawizy, Lezan; Murray-Rust, Peter; Tsujii, Junichi; Ananiadou, Sophia

2011-01-01

Chemistry text mining tools should be interoperable and adaptable regardless of system-level implementation, installation or even programming issues. We aim to abstract the functionality of these tools from the underlying implementation via reconfigurable workflows for automatically identifying chemical names. To achieve this, we refactored an established named entity recogniser (in the chemistry domain), OSCAR and studied the impact of each component on the net performance. We developed two reconfigurable workflows from OSCAR using an interoperable text mining framework, U-Compare. These workflows can be altered using the drag-&-drop mechanism of the graphical user interface of U-Compare. These workflows also provide a platform to study the relationship between text mining components such as tokenisation and named entity recognition (using maximum entropy Markov model (MEMM) and pattern recognition based classifiers). Results indicate that, for chemistry in particular, eliminating noise generated by tokenisation techniques lead to a slightly better performance than others, in terms of named entity recognition (NER) accuracy. Poor tokenisation translates into poorer input to the classifier components which in turn leads to an increase in Type I or Type II errors, thus, lowering the overall performance. On the Sciborg corpus, the workflow based system, which uses a new tokeniser whilst retaining the same MEMM component, increases the F-score from 82.35% to 84.44%. On the PubMed corpus, it recorded an F-score of 84.84% as against 84.23% by OSCAR.

Incorporating domain knowledge in chemical and biomedical named entity recognition with word representations.

PubMed

Munkhdalai, Tsendsuren; Li, Meijing; Batsuren, Khuyagbaatar; Park, Hyeon Ah; Choi, Nak Hyeon; Ryu, Keun Ho

2015-01-01

Chemical and biomedical Named Entity Recognition (NER) is an essential prerequisite task before effective text mining can begin for biochemical-text data. Exploiting unlabeled text data to leverage system performance has been an active and challenging research topic in text mining due to the recent growth in the amount of biomedical literature. We present a semi-supervised learning method that efficiently exploits unlabeled data in order to incorporate domain knowledge into a named entity recognition model and to leverage system performance. The proposed method includes Natural Language Processing (NLP) tasks for text preprocessing, learning word representation features from a large amount of text data for feature extraction, and conditional random fields for token classification. Other than the free text in the domain, the proposed method does not rely on any lexicon nor any dictionary in order to keep the system applicable to other NER tasks in bio-text data. We extended BANNER, a biomedical NER system, with the proposed method. This yields an integrated system that can be applied to chemical and drug NER or biomedical NER. We call our branch of the BANNER system BANNER-CHEMDNER, which is scalable over millions of documents, processing about 530 documents per minute, is configurable via XML, and can be plugged into other systems by using the BANNER Unstructured Information Management Architecture (UIMA) interface. BANNER-CHEMDNER achieved an 85.68% and an 86.47% F-measure on the testing sets of CHEMDNER Chemical Entity Mention (CEM) and Chemical Document Indexing (CDI) subtasks, respectively, and achieved an 87.04% F-measure on the official testing set of the BioCreative II gene mention task, showing remarkable performance in both chemical and biomedical NER. BANNER-CHEMDNER system is available at: https://bitbucket.org/tsendeemts/banner-chemdner.

Deep learning with word embeddings improves biomedical named entity recognition

PubMed Central

Habibi, Maryam; Weber, Leon; Neves, Mariana; Wiegandt, David Luis; Leser, Ulf

2017-01-01

Abstract Motivation: Text mining has become an important tool for biomedical research. The most fundamental text-mining task is the recognition of biomedical named entities (NER), such as genes, chemicals and diseases. Current NER methods rely on pre-defined features which try to capture the specific surface properties of entity types, properties of the typical local context, background knowledge, and linguistic information. State-of-the-art tools are entity-specific, as dictionaries and empirically optimal feature sets differ between entity types, which makes their development costly. Furthermore, features are often optimized for a specific gold standard corpus, which makes extrapolation of quality measures difficult. Results: We show that a completely generic method based on deep learning and statistical word embeddings [called long short-term memory network-conditional random field (LSTM-CRF)] outperforms state-of-the-art entity-specific NER tools, and often by a large margin. To this end, we compared the performance of LSTM-CRF on 33 data sets covering five different entity classes with that of best-of-class NER tools and an entity-agnostic CRF implementation. On average, F1-score of LSTM-CRF is 5% above that of the baselines, mostly due to a sharp increase in recall. Availability and implementation: The source code for LSTM-CRF is available at https://github.com/glample/tagger and the links to the corpora are available at https://corposaurus.github.io/corpora/. Contact: habibima@informatik.hu-berlin.de PMID:28881963

Segregation of anterior temporal regions critical for retrieving names of unique and nonunique entities reflects underlying long-range connectivity

PubMed Central

Mehta, Sonya; Inoue, Kayo; Rudrauf, David; Damasio, Hanna; Tranel, Daniel; Grabowski, Thomas

2015-01-01

Lesion-deficit studies support the hypothesis that the left anterior temporal lobe (ATL) plays a critical role in retrieving names of concrete entities. They further suggest that different regions of the left ATL process different conceptual categories. Here we test the specificity of these relationships and whether the anatomical segregation is related to the underlying organization of white matter connections. We reanalyzed data from a previous lesion study of naming and recognition across five categories of concrete entities. In voxelwise logistic regressions of lesion-deficit associations, we formally incorporated measures of disconnection of long-range association fiber tracts (FTs) and covaried for recognition and non-category specific naming deficits. We also performed fiber tractwise analyses to assess whether damage to specific FTs was preferentially associated with category-selective naming deficits. Damage to the basolateral ATL was associated with naming deficits for both unique (famous faces) and non-unique entities, whereas the damage to the temporal pole was associated with naming deficits for unique entities only. This segregation pattern remained after accounting for comorbid recognition deficits or naming deficits in other categories. The tractwise analyses showed that damage to the uncinate fasciculus was associated with naming impairments for unique entities, while damage to the inferior longitudinal fasciculus was associated with naming impairments for non-unique entities. Covarying for FT transection in voxelwise analyses rendered the cortical association for unique entities more focal. These results are consistent with the partial segregation of brain system support for name retrieval of unique and non-unique entities at both the level of cortical components and underlying white matter fiber bundles. Our study reconciles theoretic accounts of the functional organization of the left ATL by revealing both category-related processing and

Deep learning with word embeddings improves biomedical named entity recognition.

PubMed

Habibi, Maryam; Weber, Leon; Neves, Mariana; Wiegandt, David Luis; Leser, Ulf

2017-07-15

Text mining has become an important tool for biomedical research. The most fundamental text-mining task is the recognition of biomedical named entities (NER), such as genes, chemicals and diseases. Current NER methods rely on pre-defined features which try to capture the specific surface properties of entity types, properties of the typical local context, background knowledge, and linguistic information. State-of-the-art tools are entity-specific, as dictionaries and empirically optimal feature sets differ between entity types, which makes their development costly. Furthermore, features are often optimized for a specific gold standard corpus, which makes extrapolation of quality measures difficult. We show that a completely generic method based on deep learning and statistical word embeddings [called long short-term memory network-conditional random field (LSTM-CRF)] outperforms state-of-the-art entity-specific NER tools, and often by a large margin. To this end, we compared the performance of LSTM-CRF on 33 data sets covering five different entity classes with that of best-of-class NER tools and an entity-agnostic CRF implementation. On average, F1-score of LSTM-CRF is 5% above that of the baselines, mostly due to a sharp increase in recall. The source code for LSTM-CRF is available at https://github.com/glample/tagger and the links to the corpora are available at https://corposaurus.github.io/corpora/ . habibima@informatik.hu-berlin.de. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Impact of translation on named-entity recognition in radiology texts

PubMed Central

Pedro, Vasco

2017-01-01

Abstract Radiology reports describe the results of radiography procedures and have the potential of being a useful source of information which can bring benefits to health care systems around the world. One way to automatically extract information from the reports is by using Text Mining tools. The problem is that these tools are mostly developed for English and reports are usually written in the native language of the radiologist, which is not necessarily English. This creates an obstacle to the sharing of Radiology information between different communities. This work explores the solution of translating the reports to English before applying the Text Mining tools, probing the question of what translation approach should be used. We created MRRAD (Multilingual Radiology Research Articles Dataset), a parallel corpus of Portuguese research articles related to Radiology and a number of alternative translations (human, automatic and semi-automatic) to English. This is a novel corpus which can be used to move forward the research on this topic. Using MRRAD we studied which kind of automatic or semi-automatic translation approach is more effective on the Named-entity recognition task of finding RadLex terms in the English version of the articles. Considering the terms extracted from human translations as our gold standard, we calculated how similar to this standard were the terms extracted using other translations. We found that a completely automatic translation approach using Google leads to F-scores (between 0.861 and 0.868, depending on the extraction approach) similar to the ones obtained through a more expensive semi-automatic translation approach using Unbabel (between 0.862 and 0.870). To better understand the results we also performed a qualitative analysis of the type of errors found in the automatic and semi-automatic translations. Database URL: https://github.com/lasigeBioTM/MRRAD PMID:29220455

Segregation of anterior temporal regions critical for retrieving names of unique and non-unique entities reflects underlying long-range connectivity.

PubMed

Mehta, Sonya; Inoue, Kayo; Rudrauf, David; Damasio, Hanna; Tranel, Daniel; Grabowski, Thomas

2016-02-01

Lesion-deficit studies support the hypothesis that the left anterior temporal lobe (ATL) plays a critical role in retrieving names of concrete entities. They further suggest that different regions of the left ATL process different conceptual categories. Here we test the specificity of these relationships and whether the anatomical segregation is related to the underlying organization of white matter connections. We reanalyzed data from a previous lesion study of naming and recognition across five categories of concrete entities. In voxelwise logistic regressions of lesion-deficit associations, we formally incorporated measures of disconnection of long-range association fiber tracts (FTs) and covaried for recognition and non-category-specific naming deficits. We also performed fiber tractwise analyses to assess whether damage to specific FTs was preferentially associated with category-selective naming deficits. Damage to the basolateral ATL was associated with naming deficits for both unique (famous faces) and non-unique entities, whereas the damage to the temporal pole was associated with naming deficits for unique entities only. This segregation pattern remained after accounting for comorbid recognition deficits or naming deficits in other categories. The tractwise analyses showed that damage to the uncinate fasciculus (UNC) was associated with naming impairments for unique entities, while damage to the inferior longitudinal fasciculus (ILF) was associated with naming impairments for non-unique entities. Covarying for FT transection in voxelwise analyses rendered the cortical association for unique entities more focal. These results are consistent with the partial segregation of brain system support for name retrieval of unique and non-unique entities at both the level of cortical components and underlying white matter fiber bundles. Our study reconciles theoretic accounts of the functional organization of the left ATL by revealing both category-related processing

Boosting drug named entity recognition using an aggregate classifier.

PubMed

Korkontzelos, Ioannis; Piliouras, Dimitrios; Dowsey, Andrew W; Ananiadou, Sophia

2015-10-01

Drug named entity recognition (NER) is a critical step for complex biomedical NLP tasks such as the extraction of pharmacogenomic, pharmacodynamic and pharmacokinetic parameters. Large quantities of high quality training data are almost always a prerequisite for employing supervised machine-learning techniques to achieve high classification performance. However, the human labour needed to produce and maintain such resources is a significant limitation. In this study, we improve the performance of drug NER without relying exclusively on manual annotations. We perform drug NER using either a small gold-standard corpus (120 abstracts) or no corpus at all. In our approach, we develop a voting system to combine a number of heterogeneous models, based on dictionary knowledge, gold-standard corpora and silver annotations, to enhance performance. To improve recall, we employed genetic programming to evolve 11 regular-expression patterns that capture common drug suffixes and used them as an extra means for recognition. Our approach uses a dictionary of drug names, i.e. DrugBank, a small manually annotated corpus, i.e. the pharmacokinetic corpus, and a part of the UKPMC database, as raw biomedical text. Gold-standard and silver annotated data are used to train maximum entropy and multinomial logistic regression classifiers. Aggregating drug NER methods, based on gold-standard annotations, dictionary knowledge and patterns, improved the performance on models trained on gold-standard annotations, only, achieving a maximum F-score of 95%. In addition, combining models trained on silver annotations, dictionary knowledge and patterns are shown to achieve comparable performance to models trained exclusively on gold-standard data. The main reason appears to be the morphological similarities shared among drug names. We conclude that gold-standard data are not a hard requirement for drug NER. Combining heterogeneous models build on dictionary knowledge can achieve similar or

A neural joint model for entity and relation extraction from biomedical text.

PubMed

Li, Fei; Zhang, Meishan; Fu, Guohong; Ji, Donghong

2017-03-31

Extracting biomedical entities and their relations from text has important applications on biomedical research. Previous work primarily utilized feature-based pipeline models to process this task. Many efforts need to be made on feature engineering when feature-based models are employed. Moreover, pipeline models may suffer error propagation and are not able to utilize the interactions between subtasks. Therefore, we propose a neural joint model to extract biomedical entities as well as their relations simultaneously, and it can alleviate the problems above. Our model was evaluated on two tasks, i.e., the task of extracting adverse drug events between drug and disease entities, and the task of extracting resident relations between bacteria and location entities. Compared with the state-of-the-art systems in these tasks, our model improved the F1 scores of the first task by 5.1% in entity recognition and 8.0% in relation extraction, and that of the second task by 9.2% in relation extraction. The proposed model achieves competitive performances with less work on feature engineering. We demonstrate that the model based on neural networks is effective for biomedical entity and relation extraction. In addition, parameter sharing is an alternative method for neural models to jointly process this task. Our work can facilitate the research on biomedical text mining.

Joint Extraction of Entities and Relations Using Reinforcement Learning and Deep Learning.

PubMed

Feng, Yuntian; Zhang, Hongjun; Hao, Wenning; Chen, Gang

2017-01-01

We use both reinforcement learning and deep learning to simultaneously extract entities and relations from unstructured texts. For reinforcement learning, we model the task as a two-step decision process. Deep learning is used to automatically capture the most important information from unstructured texts, which represent the state in the decision process. By designing the reward function per step, our proposed method can pass the information of entity extraction to relation extraction and obtain feedback in order to extract entities and relations simultaneously. Firstly, we use bidirectional LSTM to model the context information, which realizes preliminary entity extraction. On the basis of the extraction results, attention based method can represent the sentences that include target entity pair to generate the initial state in the decision process. Then we use Tree-LSTM to represent relation mentions to generate the transition state in the decision process. Finally, we employ Q -Learning algorithm to get control policy π in the two-step decision process. Experiments on ACE2005 demonstrate that our method attains better performance than the state-of-the-art method and gets a 2.4% increase in recall-score.

Joint Extraction of Entities and Relations Using Reinforcement Learning and Deep Learning

PubMed Central

Zhang, Hongjun; Chen, Gang

2017-01-01

We use both reinforcement learning and deep learning to simultaneously extract entities and relations from unstructured texts. For reinforcement learning, we model the task as a two-step decision process. Deep learning is used to automatically capture the most important information from unstructured texts, which represent the state in the decision process. By designing the reward function per step, our proposed method can pass the information of entity extraction to relation extraction and obtain feedback in order to extract entities and relations simultaneously. Firstly, we use bidirectional LSTM to model the context information, which realizes preliminary entity extraction. On the basis of the extraction results, attention based method can represent the sentences that include target entity pair to generate the initial state in the decision process. Then we use Tree-LSTM to represent relation mentions to generate the transition state in the decision process. Finally, we employ Q-Learning algorithm to get control policy π in the two-step decision process. Experiments on ACE2005 demonstrate that our method attains better performance than the state-of-the-art method and gets a 2.4% increase in recall-score. PMID:28894463

Naming unique entities in the semantic variant of primary progressive aphasia and Alzheimer's disease: Towards a better understanding of the semantic impairment.

PubMed

Montembeault, M; Brambati, S M; Joubert, S; Boukadi, M; Chapleau, M; Laforce, R Jr; Wilson, M A; Macoir, J; Rouleau, I

2017-01-27

While the semantic variant of primary progressive aphasia (svPPA) is characterized by a predominant semantic memory impairment, episodic memory impairments are the clinical hallmark of Alzheimer's disease (AD). However, AD patients also present with semantic deficits, which are more severe for semantically unique entities (e.g. a famous person) than for common concepts (e.g. a beaver). Previous studies in these patient populations have largely focused on famous-person naming. Therefore, we aimed to evaluate if these impairments also extend to other semantically unique entities such as famous places and famous logos. In this study, 13 AD patients, 9 svPPA patients, and 12 cognitively unimpaired elderly subjects (CTRL) were tested with a picture-naming test of non-unique entities (Boston Naming Test) and three experimental tests of semantically unique entities assessing naming of famous persons, places, and logos. Both clinical groups were overall more impaired at naming semantically unique entities than non-unique entities. Naming impairments in AD and svPPA extended to the other types of semantically unique entities, since a CTRL>AD>svPPA pattern was found on the performance of all naming tests. Naming famous places and famous persons appeared to be most impaired in svPPA, and both specific and general semantic knowledge for these entities were affected in these patients. Although AD patients were most significantly impaired on famous-person naming, only their specific semantic knowledge was impaired, while general knowledge was preserved. Post-hoc neuroimaging analyses also showed that famous-person naming impairments in AD correlated with atrophy in the temporo-parietal junction, a region functionally associated with lexical access. In line with previous studies, svPPA patients' impairment in both naming and semantic knowledge suggest a more profound semantic impairment, while naming impairments in AD may arise to a greater extent from impaired lexical access

Assessment of disease named entity recognition on a corpus of annotated sentences.

PubMed

Jimeno, Antonio; Jimenez-Ruiz, Ernesto; Lee, Vivian; Gaudan, Sylvain; Berlanga, Rafael; Rebholz-Schuhmann, Dietrich

2008-04-11

In recent years, the recognition of semantic types from the biomedical scientific literature has been focused on named entities like protein and gene names (PGNs) and gene ontology terms (GO terms). Other semantic types like diseases have not received the same level of attention. Different solutions have been proposed to identify disease named entities in the scientific literature. While matching the terminology with language patterns suffers from low recall (e.g., Whatizit) other solutions make use of morpho-syntactic features to better cover the full scope of terminological variability (e.g., MetaMap). Currently, MetaMap that is provided from the National Library of Medicine (NLM) is the state of the art solution for the annotation of concepts from UMLS (Unified Medical Language System) in the literature. Nonetheless, its performance has not yet been assessed on an annotated corpus. In addition, little effort has been invested so far to generate an annotated dataset that links disease entities in text to disease entries in a database, thesaurus or ontology and that could serve as a gold standard to benchmark text mining solutions. As part of our research work, we have taken a corpus that has been delivered in the past for the identification of associations of genes to diseases based on the UMLS Metathesaurus and we have reprocessed and re-annotated the corpus. We have gathered annotations for disease entities from two curators, analyzed their disagreement (0.51 in the kappa-statistic) and composed a single annotated corpus for public use. Thereafter, three solutions for disease named entity recognition including MetaMap have been applied to the corpus to automatically annotate it with UMLS Metathesaurus concepts. The resulting annotations have been benchmarked to compare their performance. The annotated corpus is publicly available at ftp://ftp.ebi.ac.uk/pub/software/textmining/corpora/diseases and can serve as a benchmark to other systems. In addition, we found

Considering context: reliable entity networks through contextual relationship extraction

NASA Astrophysics Data System (ADS)

David, Peter; Hawes, Timothy; Hansen, Nichole; Nolan, James J.

2016-05-01

Existing information extraction techniques can only partially address the problem of exploiting unreadable-large amounts text. When discussion of events and relationships is limited to simple, past-tense, factual descriptions of events, current NLP-based systems can identify events and relationships and extract a limited amount of additional information. But the simple subset of available information that existing tools can extract from text is only useful to a small set of users and problems. Automated systems need to find and separate information based on what is threatened or planned to occur, has occurred in the past, or could potentially occur. We address the problem of advanced event and relationship extraction with our event and relationship attribute recognition system, which labels generic, planned, recurring, and potential events. The approach is based on a combination of new machine learning methods, novel linguistic features, and crowd-sourced labeling. The attribute labeler closes the gap between structured event and relationship models and the complicated and nuanced language that people use to describe them. Our operational-quality event and relationship attribute labeler enables Warfighters and analysts to more thoroughly exploit information in unstructured text. This is made possible through 1) More precise event and relationship interpretation, 2) More detailed information about extracted events and relationships, and 3) More reliable and informative entity networks that acknowledge the different attributes of entity-entity relationships.

Named Entity Recognition in Chinese Clinical Text Using Deep Neural Network.

PubMed

Wu, Yonghui; Jiang, Min; Lei, Jianbo; Xu, Hua

2015-01-01

Rapid growth in electronic health records (EHRs) use has led to an unprecedented expansion of available clinical data in electronic formats. However, much of the important healthcare information is locked in the narrative documents. Therefore Natural Language Processing (NLP) technologies, e.g., Named Entity Recognition that identifies boundaries and types of entities, has been extensively studied to unlock important clinical information in free text. In this study, we investigated a novel deep learning method to recognize clinical entities in Chinese clinical documents using the minimal feature engineering approach. We developed a deep neural network (DNN) to generate word embeddings from a large unlabeled corpus through unsupervised learning and another DNN for the NER task. The experiment results showed that the DNN with word embeddings trained from the large unlabeled corpus outperformed the state-of-the-art CRF's model in the minimal feature engineering setting, achieving the highest F1-score of 0.9280. Further analysis showed that word embeddings derived through unsupervised learning from large unlabeled corpus remarkably improved the DNN with randomized embedding, denoting the usefulness of unsupervised feature learning.

Network analysis of named entity co-occurrences in written texts

NASA Astrophysics Data System (ADS)

Amancio, Diego Raphael

2016-06-01

The use of methods borrowed from statistics and physics to analyze written texts has allowed the discovery of unprecedent patterns of human behavior and cognition by establishing links between models features and language structure. While current models have been useful to unveil patterns via analysis of syntactical and semantical networks, only a few works have probed the relevance of investigating the structure arising from the relationship between relevant entities such as characters, locations and organizations. In this study, we represent entities appearing in the same context as a co-occurrence network, where links are established according to a null model based on random, shuffled texts. Computational simulations performed in novels revealed that the proposed model displays interesting topological features, such as the small world feature, characterized by high values of clustering coefficient. The effectiveness of our model was verified in a practical pattern recognition task in real networks. When compared with traditional word adjacency networks, our model displayed optimized results in identifying unknown references in texts. Because the proposed representation plays a complementary role in characterizing unstructured documents via topological analysis of named entities, we believe that it could be useful to improve the characterization of written texts (and related systems), specially if combined with traditional approaches based on statistical and deeper paradigms.

Extracting biomedical events from pairs of text entities

PubMed Central

2015-01-01

Background Huge amounts of electronic biomedical documents, such as molecular biology reports or genomic papers are generated daily. Nowadays, these documents are mainly available in the form of unstructured free texts, which require heavy processing for their registration into organized databases. This organization is instrumental for information retrieval, enabling to answer the advanced queries of researchers and practitioners in biology, medicine, and related fields. Hence, the massive data flow calls for efficient automatic methods of text-mining that extract high-level information, such as biomedical events, from biomedical text. The usual computational tools of Natural Language Processing cannot be readily applied to extract these biomedical events, due to the peculiarities of the domain. Indeed, biomedical documents contain highly domain-specific jargon and syntax. These documents also describe distinctive dependencies, making text-mining in molecular biology a specific discipline. Results We address biomedical event extraction as the classification of pairs of text entities into the classes corresponding to event types. The candidate pairs of text entities are recursively provided to a multiclass classifier relying on Support Vector Machines. This recursive process extracts events involving other events as arguments. Compared to joint models based on Markov Random Fields, our model simplifies inference and hence requires shorter training and prediction times along with lower memory capacity. Compared to usual pipeline approaches, our model passes over a complex intermediate problem, while making a more extensive usage of sophisticated joint features between text entities. Our method focuses on the core event extraction of the Genia task of BioNLP challenges yielding the best result reported so far on the 2013 edition. PMID:26201478

Developing a hybrid dictionary-based bio-entity recognition technique.

PubMed

Song, Min; Yu, Hwanjo; Han, Wook-Shin

2015-01-01

Bio-entity extraction is a pivotal component for information extraction from biomedical literature. The dictionary-based bio-entity extraction is the first generation of Named Entity Recognition (NER) techniques. This paper presents a hybrid dictionary-based bio-entity extraction technique. The approach expands the bio-entity dictionary by combining different data sources and improves the recall rate through the shortest path edit distance algorithm. In addition, the proposed technique adopts text mining techniques in the merging stage of similar entities such as Part of Speech (POS) expansion, stemming, and the exploitation of the contextual cues to further improve the performance. The experimental results show that the proposed technique achieves the best or at least equivalent performance among compared techniques, GENIA, MESH, UMLS, and combinations of these three resources in F-measure. The results imply that the performance of dictionary-based extraction techniques is largely influenced by information resources used to build the dictionary. In addition, the edit distance algorithm shows steady performance with three different dictionaries in precision whereas the context-only technique achieves a high-end performance with three difference dictionaries in recall.

Developing a hybrid dictionary-based bio-entity recognition technique

PubMed Central

2015-01-01

Background Bio-entity extraction is a pivotal component for information extraction from biomedical literature. The dictionary-based bio-entity extraction is the first generation of Named Entity Recognition (NER) techniques. Methods This paper presents a hybrid dictionary-based bio-entity extraction technique. The approach expands the bio-entity dictionary by combining different data sources and improves the recall rate through the shortest path edit distance algorithm. In addition, the proposed technique adopts text mining techniques in the merging stage of similar entities such as Part of Speech (POS) expansion, stemming, and the exploitation of the contextual cues to further improve the performance. Results The experimental results show that the proposed technique achieves the best or at least equivalent performance among compared techniques, GENIA, MESH, UMLS, and combinations of these three resources in F-measure. Conclusions The results imply that the performance of dictionary-based extraction techniques is largely influenced by information resources used to build the dictionary. In addition, the edit distance algorithm shows steady performance with three different dictionaries in precision whereas the context-only technique achieves a high-end performance with three difference dictionaries in recall. PMID:26043907

TaggerOne: joint named entity recognition and normalization with semi-Markov Models

PubMed Central

Leaman, Robert; Lu, Zhiyong

2016-01-01

Motivation: Text mining is increasingly used to manage the accelerating pace of the biomedical literature. Many text mining applications depend on accurate named entity recognition (NER) and normalization (grounding). While high performing machine learning methods trainable for many entity types exist for NER, normalization methods are usually specialized to a single entity type. NER and normalization systems are also typically used in a serial pipeline, causing cascading errors and limiting the ability of the NER system to directly exploit the lexical information provided by the normalization. Methods: We propose the first machine learning model for joint NER and normalization during both training and prediction. The model is trainable for arbitrary entity types and consists of a semi-Markov structured linear classifier, with a rich feature approach for NER and supervised semantic indexing for normalization. We also introduce TaggerOne, a Java implementation of our model as a general toolkit for joint NER and normalization. TaggerOne is not specific to any entity type, requiring only annotated training data and a corresponding lexicon, and has been optimized for high throughput. Results: We validated TaggerOne with multiple gold-standard corpora containing both mention- and concept-level annotations. Benchmarking results show that TaggerOne achieves high performance on diseases (NCBI Disease corpus, NER f-score: 0.829, normalization f-score: 0.807) and chemicals (BioCreative 5 CDR corpus, NER f-score: 0.914, normalization f-score 0.895). These results compare favorably to the previous state of the art, notwithstanding the greater flexibility of the model. We conclude that jointly modeling NER and normalization greatly improves performance. Availability and Implementation: The TaggerOne source code and an online demonstration are available at: http://www.ncbi.nlm.nih.gov/bionlp/taggerone Contact: zhiyong.lu@nih.gov Supplementary information: Supplementary data are

Disambiguating the species of biomedical named entities using natural language parsers

PubMed Central

Wang, Xinglong; Tsujii, Jun'ichi; Ananiadou, Sophia

2010-01-01

Motivation: Text mining technologies have been shown to reduce the laborious work involved in organizing the vast amount of information hidden in the literature. One challenge in text mining is linking ambiguous word forms to unambiguous biological concepts. This article reports on a comprehensive study on resolving the ambiguity in mentions of biomedical named entities with respect to model organisms and presents an array of approaches, with focus on methods utilizing natural language parsers. Results: We build a corpus for organism disambiguation where every occurrence of protein/gene entity is manually tagged with a species ID, and evaluate a number of methods on it. Promising results are obtained by training a machine learning model on syntactic parse trees, which is then used to decide whether an entity belongs to the model organism denoted by a neighbouring species-indicating word (e.g. yeast). The parser-based approaches are also compared with a supervised classification method and results indicate that the former are a more favorable choice when domain portability is of concern. The best overall performance is obtained by combining the strengths of syntactic features and supervised classification. Availability: The corpus and demo are available at http://www.nactem.ac.uk/deca_details/start.cgi, and the software is freely available as U-Compare components (Kano et al., 2009): NaCTeM Species Word Detector and NaCTeM Species Disambiguator. U-Compare is available at http://-compare.org/ Contact: xinglong.wang@manchester.ac.uk PMID:20053840

TaggerOne: joint named entity recognition and normalization with semi-Markov Models.

PubMed

Leaman, Robert; Lu, Zhiyong

2016-09-15

Text mining is increasingly used to manage the accelerating pace of the biomedical literature. Many text mining applications depend on accurate named entity recognition (NER) and normalization (grounding). While high performing machine learning methods trainable for many entity types exist for NER, normalization methods are usually specialized to a single entity type. NER and normalization systems are also typically used in a serial pipeline, causing cascading errors and limiting the ability of the NER system to directly exploit the lexical information provided by the normalization. We propose the first machine learning model for joint NER and normalization during both training and prediction. The model is trainable for arbitrary entity types and consists of a semi-Markov structured linear classifier, with a rich feature approach for NER and supervised semantic indexing for normalization. We also introduce TaggerOne, a Java implementation of our model as a general toolkit for joint NER and normalization. TaggerOne is not specific to any entity type, requiring only annotated training data and a corresponding lexicon, and has been optimized for high throughput. We validated TaggerOne with multiple gold-standard corpora containing both mention- and concept-level annotations. Benchmarking results show that TaggerOne achieves high performance on diseases (NCBI Disease corpus, NER f-score: 0.829, normalization f-score: 0.807) and chemicals (BioCreative 5 CDR corpus, NER f-score: 0.914, normalization f-score 0.895). These results compare favorably to the previous state of the art, notwithstanding the greater flexibility of the model. We conclude that jointly modeling NER and normalization greatly improves performance. The TaggerOne source code and an online demonstration are available at: http://www.ncbi.nlm.nih.gov/bionlp/taggerone zhiyong.lu@nih.gov Supplementary data are available at Bioinformatics online. Published by Oxford University Press 2016. This work is written

Information extraction system

DOEpatents

Lemmond, Tracy D; Hanley, William G; Guensche, Joseph Wendell; Perry, Nathan C; Nitao, John J; Kidwell, Paul Brandon; Boakye, Kofi Agyeman; Glaser, Ron E; Prenger, Ryan James

2014-05-13

An information extraction system and methods of operating the system are provided. In particular, an information extraction system for performing meta-extraction of named entities of people, organizations, and locations as well as relationships and events from text documents are described herein.

Identifying interactions between chemical entities in biomedical text.

PubMed

Lamurias, Andre; Ferreira, João D; Couto, Francisco M

2014-10-23

Interactions between chemical compounds described in biomedical text can be of great importance to drug discovery and design, as well as pharmacovigilance. We developed a novel system, \\"Identifying Interactions between Chemical Entities\\" (IICE), to identify chemical interactions described in text. Kernel-based Support Vector Machines first identify the interactions and then an ensemble classifier validates and classifies the type of each interaction. This relation extraction module was evaluated with the corpus released for the DDI Extraction task of SemEval 2013, obtaining results comparable to state-of-the-art methods for this type of task. We integrated this module with our chemical named entity recognition module and made the whole system available as a web tool at www.lasige.di.fc.ul.pt/webtools/iice.

Identifying interactions between chemical entities in biomedical text.

PubMed

Lamurias, Andre; Ferreira, João D; Couto, Francisco M

2014-12-01

Interactions between chemical compounds described in biomedical text can be of great importance to drug discovery and design, as well as pharmacovigilance. We developed a novel system, "Identifying Interactions between Chemical Entities" (IICE), to identify chemical interactions described in text. Kernel-based Support Vector Machines first identify the interactions and then an ensemble classifier validates and classifies the type of each interaction. This relation extraction module was evaluated with the corpus released for the DDI Extraction task of SemEval 2013, obtaining results comparable to stateof- the-art methods for this type of task. We integrated this module with our chemical named entity recognition module and made the whole system available as a web tool at www.lasige.di.fc.ul.pt/webtools/iice.

A System for Identifying Named Entities in Biomedical Text: how Results From two Evaluations Reflect on Both the System and the Evaluations

PubMed Central

Dingare, Shipra; Nissim, Malvina; Finkel, Jenny; Grover, Claire

2005-01-01

We present a maximum entropy-based system for identifying named entities (NEs) in biomedical abstracts and present its performance in the only two biomedical named entity recognition (NER) comparative evaluations that have been held to date, namely BioCreative and Coling BioNLP. Our system obtained an exact match F-score of 83.2% in the BioCreative evaluation and 70.1% in the BioNLP evaluation. We discuss our system in detail, including its rich use of local features, attention to correct boundary identification, innovative use of external knowledge resources, including parsing and web searches, and rapid adaptation to new NE sets. We also discuss in depth problems with data annotation in the evaluations which caused the final performance to be lower than optimal. PMID:18629295

Improving Information Extraction and Translation Using Component Interactions

DTIC Science & Technology

2008-01-01

74 7. CASE STUDY ON MONOLINGUAL INTERACTION.....................................................................76 7.1 IMPROVING NAME TAGGING BY...interactions described above focused on the monolingual analysis pipeline. (Huang and Vogel, 2002) presented a cross-lingual joint inference example to...improve the extracted named entity translation dictionary and the entity annotation in a bilingual 22 training corpus. They used a more

Liberal Entity Extraction: Rapid Construction of Fine-Grained Entity Typing Systems.

PubMed

Huang, Lifu; May, Jonathan; Pan, Xiaoman; Ji, Heng; Ren, Xiang; Han, Jiawei; Zhao, Lin; Hendler, James A

2017-03-01

The ability of automatically recognizing and typing entities in natural language without prior knowledge (e.g., predefined entity types) is a major challenge in processing such data. Most existing entity typing systems are limited to certain domains, genres, and languages. In this article, we propose a novel unsupervised entity-typing framework by combining symbolic and distributional semantics. We start from learning three types of representations for each entity mention: general semantic representation, specific context representation, and knowledge representation based on knowledge bases. Then we develop a novel joint hierarchical clustering and linking algorithm to type all mentions using these representations. This framework does not rely on any annotated data, predefined typing schema, or handcrafted features; therefore, it can be quickly adapted to a new domain, genre, and/or language. Experiments on genres (news and discussion forum) show comparable performance with state-of-the-art supervised typing systems trained from a large amount of labeled data. Results on various languages (English, Chinese, Japanese, Hausa, and Yoruba) and domains (general and biomedical) demonstrate the portability of our framework.

Liberal Entity Extraction: Rapid Construction of Fine-Grained Entity Typing Systems

PubMed Central

Huang, Lifu; May, Jonathan; Pan, Xiaoman; Ji, Heng; Ren, Xiang; Han, Jiawei; Zhao, Lin; Hendler, James A.

2017-01-01

Abstract The ability of automatically recognizing and typing entities in natural language without prior knowledge (e.g., predefined entity types) is a major challenge in processing such data. Most existing entity typing systems are limited to certain domains, genres, and languages. In this article, we propose a novel unsupervised entity-typing framework by combining symbolic and distributional semantics. We start from learning three types of representations for each entity mention: general semantic representation, specific context representation, and knowledge representation based on knowledge bases. Then we develop a novel joint hierarchical clustering and linking algorithm to type all mentions using these representations. This framework does not rely on any annotated data, predefined typing schema, or handcrafted features; therefore, it can be quickly adapted to a new domain, genre, and/or language. Experiments on genres (news and discussion forum) show comparable performance with state-of-the-art supervised typing systems trained from a large amount of labeled data. Results on various languages (English, Chinese, Japanese, Hausa, and Yoruba) and domains (general and biomedical) demonstrate the portability of our framework. PMID:28328252

Using Ontology Fingerprints to disambiguate gene name entities in the biomedical literature

PubMed Central

Chen, Guocai; Zhao, Jieyi; Cohen, Trevor; Tao, Cui; Sun, Jingchun; Xu, Hua; Bernstam, Elmer V.; Lawson, Andrew; Zeng, Jia; Johnson, Amber M.; Holla, Vijaykumar; Bailey, Ann M.; Lara-Guerra, Humberto; Litzenburger, Beate; Meric-Bernstam, Funda; Jim Zheng, W.

2015-01-01

Ambiguous gene names in the biomedical literature are a barrier to accurate information extraction. To overcome this hurdle, we generated Ontology Fingerprints for selected genes that are relevant for personalized cancer therapy. These Ontology Fingerprints were used to evaluate the association between genes and biomedical literature to disambiguate gene names. We obtained 93.6% precision for the test gene set and 80.4% for the area under a receiver-operating characteristics curve for gene and article association. The core algorithm was implemented using a graphics processing unit-based MapReduce framework to handle big data and to improve performance. We conclude that Ontology Fingerprints can help disambiguate gene names mentioned in text and analyse the association between genes and articles. Database URL: http://www.ontologyfingerprint.org PMID:25858285

Using Ontology Fingerprints to disambiguate gene name entities in the biomedical literature.

PubMed

Chen, Guocai; Zhao, Jieyi; Cohen, Trevor; Tao, Cui; Sun, Jingchun; Xu, Hua; Bernstam, Elmer V; Lawson, Andrew; Zeng, Jia; Johnson, Amber M; Holla, Vijaykumar; Bailey, Ann M; Lara-Guerra, Humberto; Litzenburger, Beate; Meric-Bernstam, Funda; Jim Zheng, W

2015-01-01

Ambiguous gene names in the biomedical literature are a barrier to accurate information extraction. To overcome this hurdle, we generated Ontology Fingerprints for selected genes that are relevant for personalized cancer therapy. These Ontology Fingerprints were used to evaluate the association between genes and biomedical literature to disambiguate gene names. We obtained 93.6% precision for the test gene set and 80.4% for the area under a receiver-operating characteristics curve for gene and article association. The core algorithm was implemented using a graphics processing unit-based MapReduce framework to handle big data and to improve performance. We conclude that Ontology Fingerprints can help disambiguate gene names mentioned in text and analyse the association between genes and articles. Database URL: http://www.ontologyfingerprint.org © The Author(s) 2015. Published by Oxford University Press.

Mining heart disease risk factors in clinical text with named entity recognition and distributional semantic models.

PubMed

Urbain, Jay

2015-12-01

We present the design, and analyze the performance of a multi-stage natural language processing system employing named entity recognition, Bayesian statistics, and rule logic to identify and characterize heart disease risk factor events in diabetic patients over time. The system was originally developed for the 2014 i2b2 Challenges in Natural Language in Clinical Data. The system's strengths included a high level of accuracy for identifying named entities associated with heart disease risk factor events. The system's primary weakness was due to inaccuracies when characterizing the attributes of some events. For example, determining the relative time of an event with respect to the record date, whether an event is attributable to the patient's history or the patient's family history, and differentiating between current and prior smoking status. We believe these inaccuracies were due in large part to the lack of an effective approach for integrating context into our event detection model. To address these inaccuracies, we explore the addition of a distributional semantic model for characterizing contextual evidence of heart disease risk factor events. Using this semantic model, we raise our initial 2014 i2b2 Challenges in Natural Language of Clinical data F1 score of 0.838 to 0.890 and increased precision by 10.3% without use of any lexicons that might bias our results. Copyright © 2015 Elsevier Inc. All rights reserved.

NERBio: using selected word conjunctions, term normalization, and global patterns to improve biomedical named entity recognition.

PubMed

Tsai, Richard Tzong-Han; Sung, Cheng-Lung; Dai, Hong-Jie; Hung, Hsieh-Chuan; Sung, Ting-Yi; Hsu, Wen-Lian

2006-12-18

Biomedical named entity recognition (Bio-NER) is a challenging problem because, in general, biomedical named entities of the same category (e.g., proteins and genes) do not follow one standard nomenclature. They have many irregularities and sometimes appear in ambiguous contexts. In recent years, machine-learning (ML) approaches have become increasingly common and now represent the cutting edge of Bio-NER technology. This paper addresses three problems faced by ML-based Bio-NER systems. First, most ML approaches usually employ singleton features that comprise one linguistic property (e.g., the current word is capitalized) and at least one class tag (e.g., B-protein, the beginning of a protein name). However, such features may be insufficient in cases where multiple properties must be considered. Adding conjunction features that contain multiple properties can be beneficial, but it would be infeasible to include all conjunction features in an NER model since memory resources are limited and some features are ineffective. To resolve the problem, we use a sequential forward search algorithm to select an effective set of features. Second, variations in the numerical parts of biomedical terms (e.g., "2" in the biomedical term IL2) cause data sparseness and generate many redundant features. In this case, we apply numerical normalization, which solves the problem by replacing all numerals in a term with one representative numeral to help classify named entities. Third, the assignment of NE tags does not depend solely on the target word's closest neighbors, but may depend on words outside the context window (e.g., a context window of five consists of the current word plus two preceding and two subsequent words). We use global patterns generated by the Smith-Waterman local alignment algorithm to identify such structures and modify the results of our ML-based tagger. This is called pattern-based post-processing. To develop our ML-based Bio-NER system, we employ conditional

A Statistical Model for Multilingual Entity Detection and Tracking

DTIC Science & Technology

2004-01-01

tomatic Content Extraction ( ACE ) evaluation achieved top-tier results in all three evaluation languages. 1 Introduction Detecting entities, whether named...of com- bining the detected mentions into groups of references to the same object. The work presented here is motivated by the ACE eval- uation...Entropy (MaxEnt henceforth) (Berger et al., 1996) and Robust Risk Minimization (RRM henceforth) 1For a description of the ACE program see http

A Tale of Two Paradigms: Disambiguating Extracted Entities with Applications to a Digital Library and the Web

ERIC Educational Resources Information Center

Huang, Jian

2010-01-01

With the increasing wealth of information on the Web, information integration is ubiquitous as the same real-world entity may appear in a variety of forms extracted from different sources. This dissertation proposes supervised and unsupervised algorithms that are naturally integrated in a scalable framework to solve the entity resolution problem,…

Human-machine interaction to disambiguate entities in unstructured text and structured datasets

NASA Astrophysics Data System (ADS)

Ward, Kevin; Davenport, Jack

2017-05-01

Creating entity network graphs is a manual, time consuming process for an intelligence analyst. Beyond the traditional big data problems of information overload, individuals are often referred to by multiple names and shifting titles as they advance in their organizations over time which quickly makes simple string or phonetic alignment methods for entities insufficient. Conversely, automated methods for relationship extraction and entity disambiguation typically produce questionable results with no way for users to vet results, correct mistakes or influence the algorithm's future results. We present an entity disambiguation tool, DRADIS, which aims to bridge the gap between human-centric and machinecentric methods. DRADIS automatically extracts entities from multi-source datasets and models them as a complex set of attributes and relationships. Entities are disambiguated across the corpus using a hierarchical model executed in Spark allowing it to scale to operational sized data. Resolution results are presented to the analyst complete with sourcing information for each mention and relationship allowing analysts to quickly vet the correctness of results as well as correct mistakes. Corrected results are used by the system to refine the underlying model allowing analysts to optimize the general model to better deal with their operational data. Providing analysts with the ability to validate and correct the model to produce a system they can trust enables them to better focus their time on producing higher quality analysis products.

NEMO: Extraction and normalization of organization names from PubMed affiliations.

PubMed

Jonnalagadda, Siddhartha Reddy; Topham, Philip

2010-10-04

Today, there are more than 18 million articles related to biomedical research indexed in MEDLINE, and information derived from them could be used effectively to save the great amount of time and resources spent by government agencies in understanding the scientific landscape, including key opinion leaders and centers of excellence. Associating biomedical articles with organization names could significantly benefit the pharmaceutical marketing industry, health care funding agencies and public health officials and be useful for other scientists in normalizing author names, automatically creating citations, indexing articles and identifying potential resources or collaborators. Large amount of extracted information helps in disambiguating organization names using machine-learning algorithms. We propose NEMO, a system for extracting organization names in the affiliation and normalizing them to a canonical organization name. Our parsing process involves multi-layered rule matching with multiple dictionaries. The system achieves more than 98% f-score in extracting organization names. Our process of normalization that involves clustering based on local sequence alignment metrics and local learning based on finding connected components. A high precision was also observed in normalization. NEMO is the missing link in associating each biomedical paper and its authors to an organization name in its canonical form and the Geopolitical location of the organization. This research could potentially help in analyzing large social networks of organizations for landscaping a particular topic, improving performance of author disambiguation, adding weak links in the co-author network of authors, augmenting NLM's MARS system for correcting errors in OCR output of affiliation field, and automatically indexing the PubMed citations with the normalized organization name and country. Our system is available as a graphical user interface available for download along with this paper.

76 FR 28503 - Identification of Three Entities as Government of Libya Entities Pursuant to Executive Order 13566

Federal Register 2010, 2011, 2012, 2013, 2014

2011-05-17

... DEPARTMENT OF THE TREASURY Office of Foreign Assets Control Identification of Three Entities as Government of Libya Entities Pursuant to Executive Order 13566 AGENCY: Department of the Treasury. ACTION... names of three entities identified on May 5, 2011 as persons whose property and interests in property...

17 CFR 229.1107 - (Item 1107) Issuing entities.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 17 Commodity and Securities Exchanges 2 2011-04-01 2011-04-01 false (Item 1107) Issuing entities....1107 (Item 1107) Issuing entities. Provide the following information about the issuing entity: (a) State the issuing entity's name and describe the issuing entity's form of organization, including the...

17 CFR 229.1107 - (Item 1107) Issuing entities.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 17 Commodity and Securities Exchanges 2 2010-04-01 2010-04-01 false (Item 1107) Issuing entities....1107 (Item 1107) Issuing entities. Provide the following information about the issuing entity: (a) State the issuing entity's name and describe the issuing entity's form of organization, including the...

Moving Hands, Moving Entities

ERIC Educational Resources Information Center

Setti, Annalisa; Borghi, Anna M.; Tessari, Alessia

2009-01-01

In this study we investigated with a priming paradigm whether uni and bimanual actions presented as primes differently affected language processing. Animals' (self-moving entities) and plants' (not self-moving entities) names were used as targets. As prime we used grasping hands, presented both as static images and videos. The results showed an…

Improving the Accuracy of Attribute Extraction using the Relatedness between Attribute Values

NASA Astrophysics Data System (ADS)

Bollegala, Danushka; Tani, Naoki; Ishizuka, Mitsuru

Extracting attribute-values related to entities from web texts is an important step in numerous web related tasks such as information retrieval, information extraction, and entity disambiguation (namesake disambiguation). For example, for a search query that contains a personal name, we can not only return documents that contain that personal name, but if we have attribute-values such as the organization for which that person works, we can also suggest documents that contain information related to that organization, thereby improving the user's search experience. Despite numerous potential applications of attribute extraction, it remains a challenging task due to the inherent noise in web data -- often a single web page contains multiple entities and attributes. We propose a graph-based approach to select the correct attribute-values from a set of candidate attribute-values extracted for a particular entity. First, we build an undirected weighted graph in which, attribute-values are represented by nodes, and the edge that connects two nodes in the graph represents the degree of relatedness between the corresponding attribute-values. Next, we find the maximum spanning tree of this graph that connects exactly one attribute-value for each attribute-type. The proposed method outperforms previously proposed attribute extraction methods on a dataset that contains 5000 web pages.

FamPlex: a resource for entity recognition and relationship resolution of human protein families and complexes in biomedical text mining.

PubMed

Bachman, John A; Gyori, Benjamin M; Sorger, Peter K

2018-06-28

For automated reading of scientific publications to extract useful information about molecular mechanisms it is critical that genes, proteins and other entities be correctly associated with uniform identifiers, a process known as named entity linking or "grounding." Correct grounding is essential for resolving relationships among mined information, curated interaction databases, and biological datasets. The accuracy of this process is largely dependent on the availability of machine-readable resources associating synonyms and abbreviations commonly found in biomedical literature with uniform identifiers. In a task involving automated reading of ∼215,000 articles using the REACH event extraction software we found that grounding was disproportionately inaccurate for multi-protein families (e.g., "AKT") and complexes with multiple subunits (e.g."NF- κB"). To address this problem we constructed FamPlex, a manually curated resource defining protein families and complexes as they are commonly encountered in biomedical text. In FamPlex the gene-level constituents of families and complexes are defined in a flexible format allowing for multi-level, hierarchical membership. To create FamPlex, text strings corresponding to entities were identified empirically from literature and linked manually to uniform identifiers; these identifiers were also mapped to equivalent entries in multiple related databases. FamPlex also includes curated prefix and suffix patterns that improve named entity recognition and event extraction. Evaluation of REACH extractions on a test corpus of ∼54,000 articles showed that FamPlex significantly increased grounding accuracy for families and complexes (from 15 to 71%). The hierarchical organization of entities in FamPlex also made it possible to integrate otherwise unconnected mechanistic information across families, subfamilies, and individual proteins. Applications of FamPlex to the TRIPS/DRUM reading system and the Biocreative VI Bioentity

Data fusion in cyber security: first order entity extraction from common cyber data

NASA Astrophysics Data System (ADS)

Giacobe, Nicklaus A.

2012-06-01

The Joint Directors of Labs Data Fusion Process Model (JDL Model) provides a framework for how to handle sensor data to develop higher levels of inference in a complex environment. Beginning from a call to leverage data fusion techniques in intrusion detection, there have been a number of advances in the use of data fusion algorithms in this subdomain of cyber security. While it is tempting to jump directly to situation-level or threat-level refinement (levels 2 and 3) for more exciting inferences, a proper fusion process starts with lower levels of fusion in order to provide a basis for the higher fusion levels. The process begins with first order entity extraction, or the identification of important entities represented in the sensor data stream. Current cyber security operational tools and their associated data are explored for potential exploitation, identifying the first order entities that exist in the data and the properties of these entities that are described by the data. Cyber events that are represented in the data stream are added to the first order entities as their properties. This work explores typical cyber security data and the inferences that can be made at the lower fusion levels (0 and 1) with simple metrics. Depending on the types of events that are expected by the analyst, these relatively simple metrics can provide insight on their own, or could be used in fusion algorithms as a basis for higher levels of inference.

Entity recognition in the biomedical domain using a hybrid approach.

PubMed

Basaldella, Marco; Furrer, Lenz; Tasso, Carlo; Rinaldi, Fabio

2017-11-09

This article describes a high-recall, high-precision approach for the extraction of biomedical entities from scientific articles. The approach uses a two-stage pipeline, combining a dictionary-based entity recognizer with a machine-learning classifier. First, the OGER entity recognizer, which has a bias towards high recall, annotates the terms that appear in selected domain ontologies. Subsequently, the Distiller framework uses this information as a feature for a machine learning algorithm to select the relevant entities only. For this step, we compare two different supervised machine-learning algorithms: Conditional Random Fields and Neural Networks. In an in-domain evaluation using the CRAFT corpus, we test the performance of the combined systems when recognizing chemicals, cell types, cellular components, biological processes, molecular functions, organisms, proteins, and biological sequences. Our best system combines dictionary-based candidate generation with Neural-Network-based filtering. It achieves an overall precision of 86% at a recall of 60% on the named entity recognition task, and a precision of 51% at a recall of 49% on the concept recognition task. These results are to our knowledge the best reported so far in this particular task.

Towards an Obesity-Cancer Knowledge Base: Biomedical Entity Identification and Relation Detection

PubMed Central

Lossio-Ventura, Juan Antonio; Hogan, William; Modave, François; Hicks, Amanda; Hanna, Josh; Guo, Yi; He, Zhe; Bian, Jiang

2017-01-01

Obesity is associated with increased risks of various types of cancer, as well as a wide range of other chronic diseases. On the other hand, access to health information activates patient participation, and improve their health outcomes. However, existing online information on obesity and its relationship to cancer is heterogeneous ranging from pre-clinical models and case studies to mere hypothesis-based scientific arguments. A formal knowledge representation (i.e., a semantic knowledge base) would help better organizing and delivering quality health information related to obesity and cancer that consumers need. Nevertheless, current ontologies describing obesity, cancer and related entities are not designed to guide automatic knowledge base construction from heterogeneous information sources. Thus, in this paper, we present methods for named-entity recognition (NER) to extract biomedical entities from scholarly articles and for detecting if two biomedical entities are related, with the long term goal of building a obesity-cancer knowledge base. We leverage both linguistic and statistical approaches in the NER task, which supersedes the state-of-the-art results. Further, based on statistical features extracted from the sentences, our method for relation detection obtains an accuracy of 99.3% and a f-measure of 0.993. PMID:28503356

Moving beyond the Name: Defining Corporate Entities to Support Provenance-Based Access

ERIC Educational Resources Information Center

Light, Michelle

2007-01-01

The second edition of the "International Standard Archival Authority Records for Corporate Bodies, Persons, and Families (ISAAR(CPF)2)" focuses on describing entities as they exist in reality, rather than on establishing authorized terms. This change allows authority records to include multiple authorized terms representing an entity as it changed…

77 FR 61658 - Designation of Two Entities Pursuant to Executive Orders

Federal Register 2010, 2011, 2012, 2013, 2014

2012-10-10

... DEPARTMENT OF THE TREASURY Office of Foreign Assets Control Designation of Two Entities Pursuant... Treasury Department's Office of Foreign Assets Control (``OFAC'') is publishing the names of two entities....'' DATES: The designation by the Director of OFAC of the two entities named in this notice, pursuant to...

Rapid Training of Information Extraction with Local and Global Data Views

DTIC Science & Technology

2012-05-01

relation type extension system based on active learning a relation type extension system based on semi-supervised learning, and a crossdomain...bootstrapping system for domain adaptive named entity extraction. The active learning procedure adopts features extracted at the sentence level as the local

Entity-based Stochastic Analysis of Search Results for Query Expansion and Results Re-Ranking

DTIC Science & Technology

2015-11-20

pages) and struc- tured data (e.g. Linked Open Data ( LOD ) [8]) coexist in var- ious forms. An important observation is that entity names (like names of...the top-L (e.g. L = 1, 000) results are retrieved. Then, Named Entity Recognition (NER) is applied in these results for identifying LOD entities. In...the next (optional) step, more semantic information about the identified entities is retrieved from the LOD (like properties and related entities). A

Encoding of Fundamental Chemical Entities of Organic Reactivity Interest using chemical ontology and XML.

PubMed

Durairaj, Vijayasarathi; Punnaivanam, Sankar

2015-09-01

Fundamental chemical entities are identified in the context of organic reactivity and classified as appropriate concept classes namely ElectronEntity, AtomEntity, AtomGroupEntity, FunctionalGroupEntity and MolecularEntity. The entity classes and their subclasses are organized into a chemical ontology named "ChemEnt" for the purpose of assertion, restriction and modification of properties through entity relations. Individual instances of entity classes are defined and encoded as a library of chemical entities in XML. The instances of entity classes are distinguished with a unique notation and identification values in order to map them with the ontology definitions. A model GUI named Entity Table is created to view graphical representations of all the entity instances. The detection of chemical entities in chemical structures is achieved through suitable algorithms. The possibility of asserting properties to the entities at different levels and the mechanism of property flow within the hierarchical entity levels is outlined. Copyright © 2015 Elsevier Inc. All rights reserved.

Spatial distribution and influence factors of interprovincial terrestrial physical geographical names in China

NASA Astrophysics Data System (ADS)

Zhang, S.; Wang, Y.; Ju, H.

2017-12-01

The interprovincial terrestrial physical geographical entities are the key areas of regional integrated management. Based on toponomy dictionaries and different thematic maps, the attributes and the spatial extent of the interprovincial terrestrial physical geographical names (ITPGN, including terrain ITPGN and water ITPGN) were extracted. The coefficient of variation and Moran's I were combined together to measure the spatial variation and spatial association of ITPGN. The influencing factors of the distribution of ITPGN and the implications for the regional management were further discussed. The results showed that 11325 ITPGN were extracted, including 7082 terrain ITPGN and 4243 water ITPGN. Hunan Province had the largest number of ITPGN in China, and Shanghai had the smallest number. The spatial variance of the terrain ITPGN was larger than that of the water ITPGN, and the ITPGN showed a significant agglomeration phenomenon in the southern part of China. Further analysis showed that the number of ITPGN was positively related with the relative elevation and the population where the relative elevation was lower than 2000m and the population was less than 50 million. But the number of ITPGN showed a negative relationship with the two factors when their values became larger, indicating a large number of unnamed entities existed in complex terrain areas and a decreasing number of terrestrial physical geographical entities in densely populated area. Based on these analysis, we suggest the government take the ITPGN as management units to realize a balance development between different parts of the entities and strengthen the geographical names census and the nomination of unnamed interprovincial physical geographical entities. This study also demonstrated that the methods of literature survey, coefficient of variation and Moran's I can be combined to enhance the understanding of the spatial pattern of ITPGN.

Context and Domain Knowledge Enhanced Entity Spotting in Informal Text

NASA Astrophysics Data System (ADS)

Gruhl, Daniel; Nagarajan, Meena; Pieper, Jan; Robson, Christine; Sheth, Amit

This paper explores the application of restricted relationship graphs (RDF) and statistical NLP techniques to improve named entity annotation in challenging Informal English domains. We validate our approach using on-line forums discussing popular music. Named entity annotation is particularly difficult in this domain because it is characterized by a large number of ambiguous entities, such as the Madonna album "Music" or Lilly Allen's pop hit "Smile".

SPECTRa-T: machine-based data extraction and semantic searching of chemistry e-theses.

PubMed

Downing, Jim; Harvey, Matt J; Morgan, Peter B; Murray-Rust, Peter; Rzepa, Henry S; Stewart, Diana C; Tonge, Alan P; Townsend, Joe A

2010-02-22

The SPECTRa-T project has developed text-mining tools to extract named chemical entities (NCEs), such as chemical names and terms, and chemical objects (COs), e.g., experimental spectral assignments and physical chemistry properties, from electronic theses (e-theses). Although NCEs were readily identified within the two major document formats studied, only the use of structured documents enabled identification of chemical objects and their association with the relevant chemical entity (e.g., systematic chemical name). A corpus of theses was analyzed and it is shown that a high degree of semantic information can be extracted from structured documents. This integrated information has been deposited in a persistent Resource Description Framework (RDF) triple-store that allows users to conduct semantic searches. The strength and weaknesses of several document formats are reviewed.

Incremental Ontology-Based Extraction and Alignment in Semi-structured Documents

NASA Astrophysics Data System (ADS)

Thiam, Mouhamadou; Bennacer, Nacéra; Pernelle, Nathalie; Lô, Moussa

SHIRIis an ontology-based system for integration of semi-structured documents related to a specific domain. The system’s purpose is to allow users to access to relevant parts of documents as answers to their queries. SHIRI uses RDF/OWL for representation of resources and SPARQL for their querying. It relies on an automatic, unsupervised and ontology-driven approach for extraction, alignment and semantic annotation of tagged elements of documents. In this paper, we focus on the Extract-Align algorithm which exploits a set of named entity and term patterns to extract term candidates to be aligned with the ontology. It proceeds in an incremental manner in order to populate the ontology with terms describing instances of the domain and to reduce the access to extern resources such as Web. We experiment it on a HTML corpus related to call for papers in computer science and the results that we obtain are very promising. These results show how the incremental behaviour of Extract-Align algorithm enriches the ontology and the number of terms (or named entities) aligned directly with the ontology increases.

Collaborative human-machine analysis to disambiguate entities in unstructured text and structured datasets

NASA Astrophysics Data System (ADS)

Davenport, Jack H.

2016-05-01

Intelligence analysts demand rapid information fusion capabilities to develop and maintain accurate situational awareness and understanding of dynamic enemy threats in asymmetric military operations. The ability to extract relationships between people, groups, and locations from a variety of text datasets is critical to proactive decision making. The derived network of entities must be automatically created and presented to analysts to assist in decision making. DECISIVE ANALYTICS Corporation (DAC) provides capabilities to automatically extract entities, relationships between entities, semantic concepts about entities, and network models of entities from text and multi-source datasets. DAC's Natural Language Processing (NLP) Entity Analytics model entities as complex systems of attributes and interrelationships which are extracted from unstructured text via NLP algorithms. The extracted entities are automatically disambiguated via machine learning algorithms, and resolution recommendations are presented to the analyst for validation; the analyst's expertise is leveraged in this hybrid human/computer collaborative model. Military capability is enhanced by these NLP Entity Analytics because analysts can now create/update an entity profile with intelligence automatically extracted from unstructured text, thereby fusing entity knowledge from structured and unstructured data sources. Operational and sustainment costs are reduced since analysts do not have to manually tag and resolve entities.

31 CFR 306.88 - Political entities and public corporations.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 31 Money and Finance:Treasury 2 2011-07-01 2011-07-01 false Political entities and public... entities and public corporations. Securities registered in the name of, or assigned to, a State, county, city, town, village, school district or other political entity, public body or corporation, may be...

31 CFR 306.88 - Political entities and public corporations.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 31 Money and Finance: Treasury 2 2010-07-01 2010-07-01 false Political entities and public... entities and public corporations. Securities registered in the name of, or assigned to, a State, county, city, town, village, school district or other political entity, public body or corporation, may be...

Using nanoinformatics methods for automatically identifying relevant nanotoxicology entities from the literature.

PubMed

García-Remesal, Miguel; García-Ruiz, Alejandro; Pérez-Rey, David; de la Iglesia, Diana; Maojo, Víctor

2013-01-01

Nanoinformatics is an emerging research field that uses informatics techniques to collect, process, store, and retrieve data, information, and knowledge on nanoparticles, nanomaterials, and nanodevices and their potential applications in health care. In this paper, we have focused on the solutions that nanoinformatics can provide to facilitate nanotoxicology research. For this, we have taken a computational approach to automatically recognize and extract nanotoxicology-related entities from the scientific literature. The desired entities belong to four different categories: nanoparticles, routes of exposure, toxic effects, and targets. The entity recognizer was trained using a corpus that we specifically created for this purpose and was validated by two nanomedicine/nanotoxicology experts. We evaluated the performance of our entity recognizer using 10-fold cross-validation. The precisions range from 87.6% (targets) to 93.0% (routes of exposure), while recall values range from 82.6% (routes of exposure) to 87.4% (toxic effects). These results prove the feasibility of using computational approaches to reliably perform different named entity recognition (NER)-dependent tasks, such as for instance augmented reading or semantic searches. This research is a "proof of concept" that can be expanded to stimulate further developments that could assist researchers in managing data, information, and knowledge at the nanolevel, thus accelerating research in nanomedicine.

Identifying non-elliptical entity mentions in a coordinated NP with ellipses.

PubMed

Chae, Jeongmin; Jung, Younghee; Lee, Taemin; Jung, Soonyoung; Huh, Chan; Kim, Gilhan; Kim, Hyeoncheol; Oh, Heungbum

2014-02-01

Named entities in the biomedical domain are often written using a Noun Phrase (NP) along with a coordinating conjunction such as 'and' and 'or'. In addition, repeated words among named entity mentions are frequently omitted. It is often difficult to identify named entities. Although various Named Entity Recognition (NER) methods have tried to solve this problem, these methods can only deal with relatively simple elliptical patterns in coordinated NPs. We propose a new NER method for identifying non-elliptical entity mentions with simple or complex ellipses using linguistic rules and an entity mention dictionary. The GENIA and CRAFT corpora were used to evaluate the performance of the proposed system. The GENIA corpus was used to evaluate the performance of the system according to the quality of the dictionary. The GENIA corpus comprises 3434 non-elliptical entity mentions in 1585 coordinated NPs with ellipses. The system achieves 92.11% precision, 95.20% recall, and 93.63% F-score in identification of non-elliptical entity mentions in coordinated NPs. The accuracy of the system in resolving simple and complex ellipses is 94.54% and 91.95%, respectively. The CRAFT corpus was used to evaluate the performance of the system under realistic conditions. The system achieved 78.47% precision, 67.10% recall, and 72.34% F-score in coordinated NPs. The performance evaluations of the system show that it efficiently solves the problem caused by ellipses, and improves NER performance. The algorithm is implemented in PHP and the code can be downloaded from https://code.google.com/p/medtextmining/. Copyright © 2013. Published by Elsevier Inc.

A stacked sequential learning method for investigator name recognition from web-based medical articles

NASA Astrophysics Data System (ADS)

Zhang, Xiaoli; Zou, Jie; Le, Daniel X.; Thoma, George

2010-01-01

"Investigator Names" is a newly required field in MEDLINE citations. It consists of personal names listed as members of corporate organizations in an article. Extracting investigator names automatically is necessary because of the increasing volume of articles reporting collaborative biomedical research in which a large number of investigators participate. In this paper, we present an SVM-based stacked sequential learning method in a novel application - recognizing named entities such as the first and last names of investigators from online medical journal articles. Stacked sequential learning is a meta-learning algorithm which can boost any base learner. It exploits contextual information by adding the predicted labels of the surrounding tokens as features. We apply this method to tag words in text paragraphs containing investigator names, and demonstrate that stacked sequential learning improves the performance of a nonsequential base learner such as an SVM classifier.

Integrated Bio-Entity Network: A System for Biological Knowledge Discovery

PubMed Central

Bell, Lindsey; Chowdhary, Rajesh; Liu, Jun S.; Niu, Xufeng; Zhang, Jinfeng

2011-01-01

A significant part of our biological knowledge is centered on relationships between biological entities (bio-entities) such as proteins, genes, small molecules, pathways, gene ontology (GO) terms and diseases. Accumulated at an increasing speed, the information on bio-entity relationships is archived in different forms at scattered places. Most of such information is buried in scientific literature as unstructured text. Organizing heterogeneous information in a structured form not only facilitates study of biological systems using integrative approaches, but also allows discovery of new knowledge in an automatic and systematic way. In this study, we performed a large scale integration of bio-entity relationship information from both databases containing manually annotated, structured information and automatic information extraction of unstructured text in scientific literature. The relationship information we integrated in this study includes protein–protein interactions, protein/gene regulations, protein–small molecule interactions, protein–GO relationships, protein–pathway relationships, and pathway–disease relationships. The relationship information is organized in a graph data structure, named integrated bio-entity network (IBN), where the vertices are the bio-entities and edges represent their relationships. Under this framework, graph theoretic algorithms can be designed to perform various knowledge discovery tasks. We designed breadth-first search with pruning (BFSP) and most probable path (MPP) algorithms to automatically generate hypotheses—the indirect relationships with high probabilities in the network. We show that IBN can be used to generate plausible hypotheses, which not only help to better understand the complex interactions in biological systems, but also provide guidance for experimental designs. PMID:21738677

Disease named entity recognition from biomedical literature using a novel convolutional neural network.

PubMed

Zhao, Zhehuan; Yang, Zhihao; Luo, Ling; Wang, Lei; Zhang, Yin; Lin, Hongfei; Wang, Jian

2017-12-28

Automatic disease named entity recognition (DNER) is of utmost importance for development of more sophisticated BioNLP tools. However, most conventional CRF based DNER systems rely on well-designed features whose selection is labor intensive and time-consuming. Though most deep learning methods can solve NER problems with little feature engineering, they employ additional CRF layer to capture the correlation information between labels in neighborhoods which makes them much complicated. In this paper, we propose a novel multiple label convolutional neural network (MCNN) based disease NER approach. In this approach, instead of the CRF layer, a multiple label strategy (MLS) first introduced by us, is employed. First, the character-level embedding, word-level embedding and lexicon feature embedding are concatenated. Then several convolutional layers are stacked over the concatenated embedding. Finally, MLS strategy is applied to the output layer to capture the correlation information between neighboring labels. As shown by the experimental results, MCNN can achieve the state-of-the-art performance on both NCBI and CDR corpora. The proposed MCNN based disease NER method achieves the state-of-the-art performance with little feature engineering. And the experimental results show the MLS strategy's effectiveness of capturing the correlation information between labels in the neighborhood.

Noun and knowledge retrieval for biological and non-biological entities following right occipitotemporal lesions.

PubMed

Bruffaerts, Rose; De Weer, An-Sofie; De Grauwe, Sophie; Thys, Miek; Dries, Eva; Thijs, Vincent; Sunaert, Stefan; Vandenbulcke, Mathieu; De Deyne, Simon; Storms, Gerrit; Vandenberghe, Rik

2014-09-01

We investigated the critical contribution of right ventral occipitotemporal cortex to knowledge of visual and functional-associative attributes of biological and non-biological entities and how this relates to category-specificity during confrontation naming. In a consecutive series of 7 patients with lesions confined to right ventral occipitotemporal cortex, we conducted an extensive assessment of oral generation of visual-sensory and functional-associative features in response to the names of biological and nonbiological entities. Subjects also performed a confrontation naming task for these categories. Our main novel finding related to a unique case with a small lesion confined to right medial fusiform gyrus who showed disproportionate naming impairment for nonbiological versus biological entities, specifically for tools. Generation of visual and functional-associative features was preserved for biological and non-biological entities. In two other cases, who had a relatively small posterior lesion restricted to primary visual and posterior fusiform cortex, retrieval of visual attributes was disproportionately impaired compared to functional-associative attributes, in particular for biological entities. However, these cases did not show a category-specific naming deficit. Two final cases with the largest lesions showed a classical dissociation between biological versus nonbiological entities during naming, with normal feature generation performance. This is the first lesion-based evidence of a critical contribution of the right medial fusiform cortex to tool naming. Second, dissociations along the dimension of attribute type during feature generation do not co-occur with category-specificity during naming in the current patient sample. Copyright © 2014 Elsevier Ltd. All rights reserved.

Adapting Web content for low-literacy readers by using lexical elaboration and named entities labeling

NASA Astrophysics Data System (ADS)

Watanabe, W. M.; Candido, A.; Amâncio, M. A.; De Oliveira, M.; Pardo, T. A. S.; Fortes, R. P. M.; Aluísio, S. M.

2010-12-01

This paper presents an approach for assisting low-literacy readers in accessing Web online information. The "Educational FACILITA" tool is a Web content adaptation tool that provides innovative features and follows more intuitive interaction models regarding accessibility concerns. Especially, we propose an interaction model and a Web application that explore the natural language processing tasks of lexical elaboration and named entity labeling for improving Web accessibility. We report on the results obtained from a pilot study on usability analysis carried out with low-literacy users. The preliminary results show that "Educational FACILITA" improves the comprehension of text elements, although the assistance mechanisms might also confuse users when word sense ambiguity is introduced, by gathering, for a complex word, a list of synonyms with multiple meanings. This fact evokes a future solution in which the correct sense for a complex word in a sentence is identified, solving this pervasive characteristic of natural languages. The pilot study also identified that experienced computer users find the tool to be more useful than novice computer users do.

Chemical Entity Recognition and Resolution to ChEBI

PubMed Central

Grego, Tiago; Pesquita, Catia; Bastos, Hugo P.; Couto, Francisco M.

2012-01-01

Chemical entities are ubiquitous through the biomedical literature and the development of text-mining systems that can efficiently identify those entities are required. Due to the lack of available corpora and data resources, the community has focused its efforts in the development of gene and protein named entity recognition systems, but with the release of ChEBI and the availability of an annotated corpus, this task can be addressed. We developed a machine-learning-based method for chemical entity recognition and a lexical-similarity-based method for chemical entity resolution and compared them with Whatizit, a popular-dictionary-based method. Our methods outperformed the dictionary-based method in all tasks, yielding an improvement in F-measure of 20% for the entity recognition task, 2–5% for the entity-resolution task, and 15% for combined entity recognition and resolution tasks. PMID:25937941

2 CFR 170.110 - Types of entities to which this part applies.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 2 Grants and Agreements 1 2014-01-01 2014-01-01 false Types of entities to which this part applies... or receive agency awards; or (2) Receive subawards under those awards. (b) Exceptions. (1) None of... her name). (2) None of the requirements regarding reporting names and total compensation of an entity...

2 CFR 170.110 - Types of entities to which this part applies.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 2 Grants and Agreements 1 2011-01-01 2011-01-01 false Types of entities to which this part applies... or receive agency awards; or (2) Receive subawards under those awards. (b) Exceptions. (1) None of... her name). (2) None of the requirements regarding reporting names and total compensation of an entity...

Sieve-based coreference resolution enhances semi-supervised learning model for chemical-induced disease relation extraction.

PubMed

Le, Hoang-Quynh; Tran, Mai-Vu; Dang, Thanh Hai; Ha, Quang-Thuy; Collier, Nigel

2016-07-01

The BioCreative V chemical-disease relation (CDR) track was proposed to accelerate the progress of text mining in facilitating integrative understanding of chemicals, diseases and their relations. In this article, we describe an extension of our system (namely UET-CAM) that participated in the BioCreative V CDR. The original UET-CAM system's performance was ranked fourth among 18 participating systems by the BioCreative CDR track committee. In the Disease Named Entity Recognition and Normalization (DNER) phase, our system employed joint inference (decoding) with a perceptron-based named entity recognizer (NER) and a back-off model with Semantic Supervised Indexing and Skip-gram for named entity normalization. In the chemical-induced disease (CID) relation extraction phase, we proposed a pipeline that includes a coreference resolution module and a Support Vector Machine relation extraction model. The former module utilized a multi-pass sieve to extend entity recall. In this article, the UET-CAM system was improved by adding a 'silver' CID corpus to train the prediction model. This silver standard corpus of more than 50 thousand sentences was automatically built based on the Comparative Toxicogenomics Database (CTD) database. We evaluated our method on the CDR test set. Results showed that our system could reach the state of the art performance with F1 of 82.44 for the DNER task and 58.90 for the CID task. Analysis demonstrated substantial benefits of both the multi-pass sieve coreference resolution method (F1 + 4.13%) and the silver CID corpus (F1 +7.3%).Database URL: SilverCID-The silver-standard corpus for CID relation extraction is freely online available at: https://zenodo.org/record/34530 (doi:10.5281/zenodo.34530). © The Author(s) 2016. Published by Oxford University Press.

GRAM-CNN: a deep learning approach with local context for named entity recognition in biomedical text.

PubMed

Zhu, Qile; Li, Xiaolin; Conesa, Ana; Pereira, Cécile

2018-05-01

Best performing named entity recognition (NER) methods for biomedical literature are based on hand-crafted features or task-specific rules, which are costly to produce and difficult to generalize to other corpora. End-to-end neural networks achieve state-of-the-art performance without hand-crafted features and task-specific knowledge in non-biomedical NER tasks. However, in the biomedical domain, using the same architecture does not yield competitive performance compared with conventional machine learning models. We propose a novel end-to-end deep learning approach for biomedical NER tasks that leverages the local contexts based on n-gram character and word embeddings via Convolutional Neural Network (CNN). We call this approach GRAM-CNN. To automatically label a word, this method uses the local information around a word. Therefore, the GRAM-CNN method does not require any specific knowledge or feature engineering and can be theoretically applied to a wide range of existing NER problems. The GRAM-CNN approach was evaluated on three well-known biomedical datasets containing different BioNER entities. It obtained an F1-score of 87.26% on the Biocreative II dataset, 87.26% on the NCBI dataset and 72.57% on the JNLPBA dataset. Those results put GRAM-CNN in the lead of the biological NER methods. To the best of our knowledge, we are the first to apply CNN based structures to BioNER problems. The GRAM-CNN source code, datasets and pre-trained model are available online at: https://github.com/valdersoul/GRAM-CNN. andyli@ece.ufl.edu or aconesa@ufl.edu. Supplementary data are available at Bioinformatics online.

GRAM-CNN: a deep learning approach with local context for named entity recognition in biomedical text

PubMed Central

Zhu, Qile; Li, Xiaolin; Conesa, Ana; Pereira, Cécile

2018-01-01

Abstract Motivation Best performing named entity recognition (NER) methods for biomedical literature are based on hand-crafted features or task-specific rules, which are costly to produce and difficult to generalize to other corpora. End-to-end neural networks achieve state-of-the-art performance without hand-crafted features and task-specific knowledge in non-biomedical NER tasks. However, in the biomedical domain, using the same architecture does not yield competitive performance compared with conventional machine learning models. Results We propose a novel end-to-end deep learning approach for biomedical NER tasks that leverages the local contexts based on n-gram character and word embeddings via Convolutional Neural Network (CNN). We call this approach GRAM-CNN. To automatically label a word, this method uses the local information around a word. Therefore, the GRAM-CNN method does not require any specific knowledge or feature engineering and can be theoretically applied to a wide range of existing NER problems. The GRAM-CNN approach was evaluated on three well-known biomedical datasets containing different BioNER entities. It obtained an F1-score of 87.26% on the Biocreative II dataset, 87.26% on the NCBI dataset and 72.57% on the JNLPBA dataset. Those results put GRAM-CNN in the lead of the biological NER methods. To the best of our knowledge, we are the first to apply CNN based structures to BioNER problems. Availability and implementation The GRAM-CNN source code, datasets and pre-trained model are available online at: https://github.com/valdersoul/GRAM-CNN. Contact andyli@ece.ufl.edu or aconesa@ufl.edu Supplementary information Supplementary data are available at Bioinformatics online. PMID:29272325

31 CFR 306.88 - Political entities and public corporations.

Code of Federal Regulations, 2013 CFR

2013-07-01

... corporations. 306.88 Section 306.88 Money and Finance: Treasury Regulations Relating to Money and Finance... entities and public corporations. Securities registered in the name of, or assigned to, a State, county, city, town, village, school district or other political entity, public body or corporation, may be...

31 CFR 306.88 - Political entities and public corporations.

Code of Federal Regulations, 2014 CFR

2014-07-01

... corporations. 306.88 Section 306.88 Money and Finance: Treasury Regulations Relating to Money and Finance... entities and public corporations. Securities registered in the name of, or assigned to, a State, county, city, town, village, school district or other political entity, public body or corporation, may be...

31 CFR 306.88 - Political entities and public corporations.

Code of Federal Regulations, 2012 CFR

2012-07-01

... corporations. 306.88 Section 306.88 Money and Finance: Treasury Regulations Relating to Money and Finance... entities and public corporations. Securities registered in the name of, or assigned to, a State, county, city, town, village, school district or other political entity, public body or corporation, may be...

Renal effects of Mammea africana Sabine (Guttiferae) stem bark methanol/methylene chloride extract on L-NAME hypertensive rats.

PubMed

Nguelefack-Mbuyo, Elvine Pami; Dimo, Théophile; Nguelefack, Télesphore Benoit; Dongmo, Alain Bertrand; Kamtchouing, Pierre; Kamanyi, Albert

2010-08-01

The present study aims at evaluating the effects of methanol/methylene chloride extract of the stem bark of Mammea africana on the renal function of L-NAME treated rats. Normotensive male Wistar rats were divided into five groups respectively treated with distilled water, L-NAME (40 mg/kg/day), L-NAME + L-arginine (100 mg/kg/day), L-NAME + captopril (20 mg/kg/day) or L-NAME + M. africana extract (200 mg/kg/day) for 30 days. Systolic blood pressure was measured before and at the end of treatment. Body weight was measured at the end of each week. Urine was collected 6 and 24 h after the first administration and further on day 15 and 30 of treatment for creatinine, sodium and potassium quantification, while plasma was collected at the end of treatment for the creatinine assay. ANOVA two way followed by Bonferonni or one way followed by Tukey were used for statistical analysis. M. africana successfully prevented the rise in blood pressure and the acute natriuresis and diuresis induced by L-NAME. When given chronically, the extract produced a sustained antinatriuretic effect, a non-significant increase in urine excretion and reduced the glomerular hyperfiltration induced by L-NAME. The above results suggest that the methanol/methylene chloride extract of the stem bark of M. africana may protect kidney against renal dysfunction and further demonstrate that its antihypertensive effect does not depend on a diuretic or natriuretic activity.

Named Entity Recognition in a Hungarian NL Based QA System

NASA Astrophysics Data System (ADS)

Tikkl, Domonkos; Szidarovszky, P. Ferenc; Kardkovacs, Zsolt T.; Magyar, Gábor

In WoW project our purpose is to create a complex search interface with the following features: search in the deep web content of contracted partners' databases, processing Hungarian natural language (NL) questions and transforming them to SQL queries for database access, image search supported by a visual thesaurus that describes in a structural form the visual content of images (also in Hungarian). This paper primarily focuses on a particular problem of question processing task: the entity recognition. Before going into details we give a short overview of the project's aims.

Gimli: open source and high-performance biomedical name recognition

PubMed Central

2013-01-01

Background Automatic recognition of biomedical names is an essential task in biomedical information extraction, presenting several complex and unsolved challenges. In recent years, various solutions have been implemented to tackle this problem. However, limitations regarding system characteristics, customization and usability still hinder their wider application outside text mining research. Results We present Gimli, an open-source, state-of-the-art tool for automatic recognition of biomedical names. Gimli includes an extended set of implemented and user-selectable features, such as orthographic, morphological, linguistic-based, conjunctions and dictionary-based. A simple and fast method to combine different trained models is also provided. Gimli achieves an F-measure of 87.17% on GENETAG and 72.23% on JNLPBA corpus, significantly outperforming existing open-source solutions. Conclusions Gimli is an off-the-shelf, ready to use tool for named-entity recognition, providing trained and optimized models for recognition of biomedical entities from scientific text. It can be used as a command line tool, offering full functionality, including training of new models and customization of the feature set and model parameters through a configuration file. Advanced users can integrate Gimli in their text mining workflows through the provided library, and extend or adapt its functionalities. Based on the underlying system characteristics and functionality, both for final users and developers, and on the reported performance results, we believe that Gimli is a state-of-the-art solution for biomedical NER, contributing to faster and better research in the field. Gimli is freely available at http://bioinformatics.ua.pt/gimli. PMID:23413997

76 FR 52384 - Designation of Additional Entities Pursuant to Executive Order 13405

Federal Register 2010, 2011, 2012, 2013, 2014

2011-08-22

... DEPARTMENT OF THE TREASURY Office of Foreign Assets Control Designation of Additional Entities... Assets Control (``OFAC'') is publishing the names of four newly-designated entities whose property and... the Director of OFAC of the four entities identified in this notice, pursuant to Executive [[Page...

A Novel Approach towards Medical Entity Recognition in Chinese Clinical Text

PubMed Central

Yu, Jian

2017-01-01

Medical entity recognition, a basic task in the language processing of clinical data, has been extensively studied in analyzing admission notes in alphabetic languages such as English. However, much less work has been done on nonstructural texts that are written in Chinese, or in the setting of differentiation of Chinese drug names between traditional Chinese medicine and Western medicine. Here, we propose a novel cascade-type Chinese medication entity recognition approach that aims at integrating the sentence category classifier from a support vector machine and the conditional random field-based medication entity recognition. We hypothesized that this approach could avoid the side effects of abundant negative samples and improve the performance of the named entity recognition from admission notes written in Chinese. Therefore, we applied this approach to a test set of 324 Chinese-written admission notes with manual annotation by medical experts. Our data demonstrated that this approach had a score of 94.2% in precision, 92.8% in recall, and 93.5% in F-measure for the recognition of traditional Chinese medicine drug names and 91.2% in precision, 92.6% in recall, and 91.7% F-measure for the recognition of Western medicine drug names. The differences in F-measure were significant compared with those in the baseline systems. PMID:29065612

Anatomical Entity Recognition with a Hierarchical Framework Augmented by External Resources

PubMed Central

Xu, Yan; Hua, Ji; Ni, Zhaoheng; Chen, Qinlang; Fan, Yubo; Ananiadou, Sophia; Chang, Eric I-Chao; Tsujii, Junichi

2014-01-01

References to anatomical entities in medical records consist not only of explicit references to anatomical locations, but also other diverse types of expressions, such as specific diseases, clinical tests, clinical treatments, which constitute implicit references to anatomical entities. In order to identify these implicit anatomical entities, we propose a hierarchical framework, in which two layers of named entity recognizers (NERs) work in a cooperative manner. Each of the NERs is implemented using the Conditional Random Fields (CRF) model, which use a range of external resources to generate features. We constructed a dictionary of anatomical entity expressions by exploiting four existing resources, i.e., UMLS, MeSH, RadLex and BodyPart3D, and supplemented information from two external knowledge bases, i.e., Wikipedia and WordNet, to improve inference of anatomical entities from implicit expressions. Experiments conducted on 300 discharge summaries showed a micro-averaged performance of 0.8509 Precision, 0.7796 Recall and 0.8137 F1 for explicit anatomical entity recognition, and 0.8695 Precision, 0.6893 Recall and 0.7690 F1 for implicit anatomical entity recognition. The use of the hierarchical framework, which combines the recognition of named entities of various types (diseases, clinical tests, treatments) with information embedded in external knowledge bases, resulted in a 5.08% increment in F1. The resources constructed for this research will be made publicly available. PMID:25343498

Identification of related gene/protein names based on an HMM of name variations.

PubMed

Yeganova, L; Smith, L; Wilbur, W J

2004-04-01

Gene and protein names follow few, if any, true naming conventions and are subject to great variation in different occurrences of the same name. This gives rise to two important problems in natural language processing. First, can one locate the names of genes or proteins in free text, and second, can one determine when two names denote the same gene or protein? The first of these problems is a special case of the problem of named entity recognition, while the second is a special case of the problem of automatic term recognition (ATR). We study the second problem, that of gene or protein name variation. Here we describe a system which, given a query gene or protein name, identifies related gene or protein names in a large list. The system is based on a dynamic programming algorithm for sequence alignment in which the mutation matrix is allowed to vary under the control of a fully trainable hidden Markov model.

The left temporal pole is a heteromodal hub for retrieving proper names

PubMed Central

Waldron, Eric J.; Manzel, Kenneth; Tranel, Daniel

2015-01-01

The left temporal pole (LTP) has been posited to be a heteromodal hub for retrieving proper names for semantically unique entities. Previous investigations have demonstrated that LTP is important for retrieving names for famous faces and unique landmarks. However, whether such a relationship would hold for unique entities apprehended through stimulus modalities other than vision has not been well established, and such evidence is critical to adjudicate claims about the “heteromodal” nature of the LTP. Here, we tested the hypothesis that the LTP would be important for naming famous voices. Individuals with LTP lesions were asked to recognize and name famous persons speaking in audio clips. Relative to neurologically normal and brain damaged comparison participants, patients with LTP lesions were able to recognize famous persons from their voices normally, but were selectively impaired in naming famous persons from their voices. The current results extend previous research and provide further support for the notion that the LTP is a convergence region serving as a heteromodal hub for retrieving the names of semantically unique entities. PMID:24389260

Recognition of chemical entities: combining dictionary-based and grammar-based approaches.

PubMed

Akhondi, Saber A; Hettne, Kristina M; van der Horst, Eelke; van Mulligen, Erik M; Kors, Jan A

2015-01-01

The past decade has seen an upsurge in the number of publications in chemistry. The ever-swelling volume of available documents makes it increasingly hard to extract relevant new information from such unstructured texts. The BioCreative CHEMDNER challenge invites the development of systems for the automatic recognition of chemicals in text (CEM task) and for ranking the recognized compounds at the document level (CDI task). We investigated an ensemble approach where dictionary-based named entity recognition is used along with grammar-based recognizers to extract compounds from text. We assessed the performance of ten different commercial and publicly available lexical resources using an open source indexing system (Peregrine), in combination with three different chemical compound recognizers and a set of regular expressions to recognize chemical database identifiers. The effect of different stop-word lists, case-sensitivity matching, and use of chunking information was also investigated. We focused on lexical resources that provide chemical structure information. To rank the different compounds found in a text, we used a term confidence score based on the normalized ratio of the term frequencies in chemical and non-chemical journals. The use of stop-word lists greatly improved the performance of the dictionary-based recognition, but there was no additional benefit from using chunking information. A combination of ChEBI and HMDB as lexical resources, the LeadMine tool for grammar-based recognition, and the regular expressions, outperformed any of the individual systems. On the test set, the F-scores were 77.8% (recall 71.2%, precision 85.8%) for the CEM task and 77.6% (recall 71.7%, precision 84.6%) for the CDI task. Missed terms were mainly due to tokenization issues, poor recognition of formulas, and term conjunctions. We developed an ensemble system that combines dictionary-based and grammar-based approaches for chemical named entity recognition, outperforming

Recognition of chemical entities: combining dictionary-based and grammar-based approaches

PubMed Central

2015-01-01

Background The past decade has seen an upsurge in the number of publications in chemistry. The ever-swelling volume of available documents makes it increasingly hard to extract relevant new information from such unstructured texts. The BioCreative CHEMDNER challenge invites the development of systems for the automatic recognition of chemicals in text (CEM task) and for ranking the recognized compounds at the document level (CDI task). We investigated an ensemble approach where dictionary-based named entity recognition is used along with grammar-based recognizers to extract compounds from text. We assessed the performance of ten different commercial and publicly available lexical resources using an open source indexing system (Peregrine), in combination with three different chemical compound recognizers and a set of regular expressions to recognize chemical database identifiers. The effect of different stop-word lists, case-sensitivity matching, and use of chunking information was also investigated. We focused on lexical resources that provide chemical structure information. To rank the different compounds found in a text, we used a term confidence score based on the normalized ratio of the term frequencies in chemical and non-chemical journals. Results The use of stop-word lists greatly improved the performance of the dictionary-based recognition, but there was no additional benefit from using chunking information. A combination of ChEBI and HMDB as lexical resources, the LeadMine tool for grammar-based recognition, and the regular expressions, outperformed any of the individual systems. On the test set, the F-scores were 77.8% (recall 71.2%, precision 85.8%) for the CEM task and 77.6% (recall 71.7%, precision 84.6%) for the CDI task. Missed terms were mainly due to tokenization issues, poor recognition of formulas, and term conjunctions. Conclusions We developed an ensemble system that combines dictionary-based and grammar-based approaches for chemical named

77 FR 31806 - Changes to Implement Micro Entity Status for Paying Patent Fees

Federal Register 2010, 2011, 2012, 2013, 2014

2012-05-30

... legislative history of 35 U.S.C. 123 is clear that it is directed to a subset of small entities, namely... history do not, for example, contemplate a for-profit, large entity applicant becoming a ``micro entity... across government agencies and identified goals designed to promote innovation; (8) considered approaches...

EXTRACT: interactive extraction of environment metadata and term suggestion for metagenomic sample annotation.

PubMed

Pafilis, Evangelos; Buttigieg, Pier Luigi; Ferrell, Barbra; Pereira, Emiliano; Schnetzer, Julia; Arvanitidis, Christos; Jensen, Lars Juhl

2016-01-01

The microbial and molecular ecology research communities have made substantial progress on developing standards for annotating samples with environment metadata. However, sample manual annotation is a highly labor intensive process and requires familiarity with the terminologies used. We have therefore developed an interactive annotation tool, EXTRACT, which helps curators identify and extract standard-compliant terms for annotation of metagenomic records and other samples. Behind its web-based user interface, the system combines published methods for named entity recognition of environment, organism, tissue and disease terms. The evaluators in the BioCreative V Interactive Annotation Task found the system to be intuitive, useful, well documented and sufficiently accurate to be helpful in spotting relevant text passages and extracting organism and environment terms. Comparison of fully manual and text-mining-assisted curation revealed that EXTRACT speeds up annotation by 15-25% and helps curators to detect terms that would otherwise have been missed. Database URL: https://extract.hcmr.gr/. © The Author(s) 2016. Published by Oxford University Press.

Enhancing of chemical compound and drug name recognition using representative tag scheme and fine-grained tokenization.

PubMed

Dai, Hong-Jie; Lai, Po-Ting; Chang, Yung-Chun; Tsai, Richard Tzong-Han

2015-01-01

The functions of chemical compounds and drugs that affect biological processes and their particular effect on the onset and treatment of diseases have attracted increasing interest with the advancement of research in the life sciences. To extract knowledge from the extensive literatures on such compounds and drugs, the organizers of BioCreative IV administered the CHEMical Compound and Drug Named Entity Recognition (CHEMDNER) task to establish a standard dataset for evaluating state-of-the-art chemical entity recognition methods. This study introduces the approach of our CHEMDNER system. Instead of emphasizing the development of novel feature sets for machine learning, this study investigates the effect of various tag schemes on the recognition of the names of chemicals and drugs by using conditional random fields. Experiments were conducted using combinations of different tokenization strategies and tag schemes to investigate the effects of tag set selection and tokenization method on the CHEMDNER task. This study presents the performance of CHEMDNER of three more representative tag schemes-IOBE, IOBES, and IOB12E-when applied to a widely utilized IOB tag set and combined with the coarse-/fine-grained tokenization methods. The experimental results thus reveal that the fine-grained tokenization strategy performance best in terms of precision, recall and F-scores when the IOBES tag set was utilized. The IOBES model with fine-grained tokenization yielded the best-F-scores in the six chemical entity categories other than the "Multiple" entity category. Nonetheless, no significant improvement was observed when a more representative tag schemes was used with the coarse or fine-grained tokenization rules. The best F-scores that were achieved using the developed system on the test dataset of the CHEMDNER task were 0.833 and 0.815 for the chemical documents indexing and the chemical entity mention recognition tasks, respectively. The results herein highlight the importance

Recognizing the Emotional Valence of Names: An ERP Study

ERIC Educational Resources Information Center

Wang, Lin; Zhu, Zude; Bastiaansen, Marcel; Hagoort, Peter; Yang, Yufang

2013-01-01

Unlike common nouns, person names refer to unique entities and generally have a referring function. We used event-related potentials to investigate the time course of identifying the emotional meaning of nouns and names. The emotional valence of names and nouns were manipulated separately. The results show early N1 effects in response to emotional…

ECO: A Framework for Entity Co-Occurrence Exploration with Faceted Navigation

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

Halliday, K. D.

2010-08-20

Even as highly structured databases and semantic knowledge bases become more prevalent, a substantial amount of human knowledge is reported as written prose. Typical textual reports, such as news articles, contain information about entities (people, organizations, and locations) and their relationships. Automatically extracting such relationships from large text corpora is a key component of corporate and government knowledge bases. The primary goal of the ECO project is to develop a scalable framework for extracting and presenting these relationships for exploration using an easily navigable faceted user interface. ECO uses entity co-occurrence relationships to identify related entities. The system aggregates andmore » indexes information on each entity pair, allowing the user to rapidly discover and mine relational information.« less

Recurrent neural networks with specialized word embeddings for health-domain named-entity recognition.

PubMed

Jauregi Unanue, Iñigo; Zare Borzeshi, Ehsan; Piccardi, Massimo

2017-12-01

Previous state-of-the-art systems on Drug Name Recognition (DNR) and Clinical Concept Extraction (CCE) have focused on a combination of text "feature engineering" and conventional machine learning algorithms such as conditional random fields and support vector machines. However, developing good features is inherently heavily time-consuming. Conversely, more modern machine learning approaches such as recurrent neural networks (RNNs) have proved capable of automatically learning effective features from either random assignments or automated word "embeddings". (i) To create a highly accurate DNR and CCE system that avoids conventional, time-consuming feature engineering. (ii) To create richer, more specialized word embeddings by using health domain datasets such as MIMIC-III. (iii) To evaluate our systems over three contemporary datasets. Two deep learning methods, namely the Bidirectional LSTM and the Bidirectional LSTM-CRF, are evaluated. A CRF model is set as the baseline to compare the deep learning systems to a traditional machine learning approach. The same features are used for all the models. We have obtained the best results with the Bidirectional LSTM-CRF model, which has outperformed all previously proposed systems. The specialized embeddings have helped to cover unusual words in DrugBank and MedLine, but not in the i2b2/VA dataset. We present a state-of-the-art system for DNR and CCE. Automated word embeddings has allowed us to avoid costly feature engineering and achieve higher accuracy. Nevertheless, the embeddings need to be retrained over datasets that are adequate for the domain, in order to adequately cover the domain-specific vocabulary. Copyright © 2017 Elsevier Inc. All rights reserved.

LeadMine: a grammar and dictionary driven approach to entity recognition.

PubMed

Lowe, Daniel M; Sayle, Roger A

2015-01-01

Chemical entity recognition has traditionally been performed by machine learning approaches. Here we describe an approach using grammars and dictionaries. This approach has the advantage that the entities found can be directly related to a given grammar or dictionary, which allows the type of an entity to be known and, if an entity is misannotated, indicates which resource should be corrected. As recognition is driven by what is expected, if spelling errors occur, they can be corrected. Correcting such errors is highly useful when attempting to lookup an entity in a database or, in the case of chemical names, converting them to structures. Our system uses a mixture of expertly curated grammars and dictionaries, as well as dictionaries automatically derived from public resources. We show that the heuristics developed to filter our dictionary of trivial chemical names (from PubChem) yields a better performing dictionary than the previously published Jochem dictionary. Our final system performs post-processing steps to modify the boundaries of entities and to detect abbreviations. These steps are shown to significantly improve performance (2.6% and 4.0% F1-score respectively). Our complete system, with incremental post-BioCreative workshop improvements, achieves 89.9% precision and 85.4% recall (87.6% F1-score) on the CHEMDNER test set. Grammar and dictionary approaches can produce results at least as good as the current state of the art in machine learning approaches. While machine learning approaches are commonly thought of as "black box" systems, our approach directly links the output entities to the input dictionaries and grammars. Our approach also allows correction of errors in detected entities, which can assist with entity resolution.

EXTRACT: Interactive extraction of environment metadata and term suggestion for metagenomic sample annotation

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

Pafilis, Evangelos; Buttigieg, Pier Luigi; Ferrell, Barbra

The microbial and molecular ecology research communities have made substantial progress on developing standards for annotating samples with environment metadata. However, sample manual annotation is a highly labor intensive process and requires familiarity with the terminologies used. We have therefore developed an interactive annotation tool, EXTRACT, which helps curators identify and extract standard-compliant terms for annotation of metagenomic records and other samples. Behind its web-based user interface, the system combines published methods for named entity recognition of environment, organism, tissue and disease terms. The evaluators in the BioCreative V Interactive Annotation Task found the system to be intuitive, useful, wellmore » documented and sufficiently accurate to be helpful in spotting relevant text passages and extracting organism and environment terms. Here the comparison of fully manual and text-mining-assisted curation revealed that EXTRACT speeds up annotation by 15–25% and helps curators to detect terms that would otherwise have been missed.« less

EXTRACT: Interactive extraction of environment metadata and term suggestion for metagenomic sample annotation

DOE PAGES

Pafilis, Evangelos; Buttigieg, Pier Luigi; Ferrell, Barbra; ...

2016-01-01

The microbial and molecular ecology research communities have made substantial progress on developing standards for annotating samples with environment metadata. However, sample manual annotation is a highly labor intensive process and requires familiarity with the terminologies used. We have therefore developed an interactive annotation tool, EXTRACT, which helps curators identify and extract standard-compliant terms for annotation of metagenomic records and other samples. Behind its web-based user interface, the system combines published methods for named entity recognition of environment, organism, tissue and disease terms. The evaluators in the BioCreative V Interactive Annotation Task found the system to be intuitive, useful, wellmore » documented and sufficiently accurate to be helpful in spotting relevant text passages and extracting organism and environment terms. Here the comparison of fully manual and text-mining-assisted curation revealed that EXTRACT speeds up annotation by 15–25% and helps curators to detect terms that would otherwise have been missed.« less

Lifelong-RL: Lifelong Relaxation Labeling for Separating Entities and Aspects in Opinion Targets.

PubMed

Shu, Lei; Liu, Bing; Xu, Hu; Kim, Annice

2016-11-01

It is well-known that opinions have targets. Extracting such targets is an important problem of opinion mining because without knowing the target of an opinion, the opinion is of limited use. So far many algorithms have been proposed to extract opinion targets. However, an opinion target can be an entity or an aspect (part or attribute) of an entity. An opinion about an entity is an opinion about the entity as a whole, while an opinion about an aspect is just an opinion about that specific attribute or aspect of an entity. Thus, opinion targets should be separated into entities and aspects before use because they represent very different things about opinions. This paper proposes a novel algorithm, called Lifelong-RL , to solve the problem based on lifelong machine learning and relaxation labeling . Extensive experiments show that the proposed algorithm Lifelong-RL outperforms baseline methods markedly.

CheNER: a tool for the identification of chemical entities and their classes in biomedical literature.

PubMed

Usié, Anabel; Cruz, Joaquim; Comas, Jorge; Solsona, Francesc; Alves, Rui

2015-01-01

Small chemical molecules regulate biological processes at the molecular level. Those molecules are often involved in causing or treating pathological states. Automatically identifying such molecules in biomedical text is difficult due to both, the diverse morphology of chemical names and the alternative types of nomenclature that are simultaneously used to describe them. To address these issues, the last BioCreAtIvE challenge proposed a CHEMDNER task, which is a Named Entity Recognition (NER) challenge that aims at labelling different types of chemical names in biomedical text. To address this challenge we tested various approaches to recognizing chemical entities in biomedical documents. These approaches range from linear Conditional Random Fields (CRFs) to a combination of CRFs with regular expression and dictionary matching, followed by a post-processing step to tag those chemical names in a corpus of Medline abstracts. We named our best performing systems CheNER. We evaluate the performance of the various approaches using the F-score statistics. Higher F-scores indicate better performance. The highest F-score we obtain in identifying unique chemical entities is 72.88%. The highest F-score we obtain in identifying all chemical entities is 73.07%. We also evaluate the F-Score of combining our system with ChemSpot, and find an increase from 72.88% to 73.83%. CheNER presents a valid alternative for automated annotation of chemical entities in biomedical documents. In addition, CheNER may be used to derive new features to train newer methods for tagging chemical entities. CheNER can be downloaded from http://metres.udl.cat and included in text annotation pipelines.

LeadMine: a grammar and dictionary driven approach to entity recognition

PubMed Central

2015-01-01

Background Chemical entity recognition has traditionally been performed by machine learning approaches. Here we describe an approach using grammars and dictionaries. This approach has the advantage that the entities found can be directly related to a given grammar or dictionary, which allows the type of an entity to be known and, if an entity is misannotated, indicates which resource should be corrected. As recognition is driven by what is expected, if spelling errors occur, they can be corrected. Correcting such errors is highly useful when attempting to lookup an entity in a database or, in the case of chemical names, converting them to structures. Results Our system uses a mixture of expertly curated grammars and dictionaries, as well as dictionaries automatically derived from public resources. We show that the heuristics developed to filter our dictionary of trivial chemical names (from PubChem) yields a better performing dictionary than the previously published Jochem dictionary. Our final system performs post-processing steps to modify the boundaries of entities and to detect abbreviations. These steps are shown to significantly improve performance (2.6% and 4.0% F1-score respectively). Our complete system, with incremental post-BioCreative workshop improvements, achieves 89.9% precision and 85.4% recall (87.6% F1-score) on the CHEMDNER test set. Conclusions Grammar and dictionary approaches can produce results at least as good as the current state of the art in machine learning approaches. While machine learning approaches are commonly thought of as "black box" systems, our approach directly links the output entities to the input dictionaries and grammars. Our approach also allows correction of errors in detected entities, which can assist with entity resolution. PMID:25810776

41 CFR 102-173.50 - What is the naming convention for States?

Code of Federal Regulations, 2014 CFR

2014-01-01

...-INTERNET GOV DOMAIN Registration § 102-173.50 What is the naming convention for States? (a) To register any second-level domain within dot-gov, State government entities must register the full State name or clearly indicate the State postal code within the name. Examples of acceptable names include virginia.gov...

41 CFR 102-173.50 - What is the naming convention for States?

Code of Federal Regulations, 2011 CFR

2011-01-01

...-INTERNET GOV DOMAIN Registration § 102-173.50 What is the naming convention for States? (a) To register any second-level domain within dot-gov, State government entities must register the full State name or clearly indicate the State postal code within the name. Examples of acceptable names include virginia.gov...

41 CFR 102-173.50 - What is the naming convention for States?

Code of Federal Regulations, 2010 CFR

2010-07-01

...-INTERNET GOV DOMAIN Registration § 102-173.50 What is the naming convention for States? (a) To register any second-level domain within dot-gov, State government entities must register the full State name or clearly indicate the State postal code within the name. Examples of acceptable names include virginia.gov...

41 CFR 102-173.50 - What is the naming convention for States?

Code of Federal Regulations, 2013 CFR

2013-07-01

...-INTERNET GOV DOMAIN Registration § 102-173.50 What is the naming convention for States? (a) To register any second-level domain within dot-gov, State government entities must register the full State name or clearly indicate the State postal code within the name. Examples of acceptable names include virginia.gov...

41 CFR 102-173.50 - What is the naming convention for States?

Code of Federal Regulations, 2012 CFR

2012-01-01

...-INTERNET GOV DOMAIN Registration § 102-173.50 What is the naming convention for States? (a) To register any second-level domain within dot-gov, State government entities must register the full State name or clearly indicate the State postal code within the name. Examples of acceptable names include virginia.gov...

Classifying Web Pages by Using Knowledge Bases for Entity Retrieval

NASA Astrophysics Data System (ADS)

Kiritani, Yusuke; Ma, Qiang; Yoshikawa, Masatoshi

In this paper, we propose a novel method to classify Web pages by using knowledge bases for entity search, which is a kind of typical Web search for information related to a person, location or organization. First, we map a Web page to entities according to the similarities between the page and the entities. Various methods for computing such similarity are applied. For example, we can compute the similarity between a given page and a Wikipedia article describing a certain entity. The frequency of an entity appearing in the page is another factor used in computing the similarity. Second, we construct a directed acyclic graph, named PEC graph, based on the relations among Web pages, entities, and categories, by referring to YAGO, a knowledge base built on Wikipedia and WordNet. Finally, by analyzing the PEC graph, we classify Web pages into categories. The results of some preliminary experiments validate the methods proposed in this paper.

Evaluation and Cross-Comparison of Lexical Entities of Biological Interest (LexEBI)

PubMed Central

Rebholz-Schuhmann, Dietrich; Kim, Jee-Hyub; Yan, Ying; Dixit, Abhishek; Friteyre, Caroline; Hoehndorf, Robert; Backofen, Rolf; Lewin, Ian

2013-01-01

Motivation Biomedical entities, their identifiers and names, are essential in the representation of biomedical facts and knowledge. In the same way, the complete set of biomedical and chemical terms, i.e. the biomedical “term space” (the “Lexeome”), forms a key resource to achieve the full integration of the scientific literature with biomedical data resources: any identified named entity can immediately be normalized to the correct database entry. This goal does not only require that we are aware of all existing terms, but would also profit from knowing all their senses and their semantic interpretation (ambiguities, nestedness). Result This study compiles a resource for lexical terms of biomedical interest in a standard format (called “LexEBI”), determines the overall number of terms, their reuse in different resources and the nestedness of terms. LexEBI comprises references for protein and gene entries and their term variants and chemical entities amongst other terms. In addition, disease terms have been identified from Medline and PubmedCentral and added to LexEBI. Our analysis demonstrates that the baseforms of terms from the different semantic types show only little polysemous use. Nonetheless, the term variants of protein and gene names (PGNs) frequently contain species mentions, which should have been avoided according to protein annotation guidelines. Furthermore, the protein and gene entities as well as the chemical entities, both do comprise enzymes leading to hierarchical polysemy, and a large portion of PGNs make reference to a chemical entity. Altogether, according to our analysis based on the Medline distribution, 401,869 unique PGNs in the documents contain a reference to 25,022 chemical entities, 3,125 disease terms or 1,576 species mentions. Conclusion LexEBI delivers the complete biomedical and chemical Lexeome in a standardized representation (http://www.ebi.ac.uk/Rebholz-srv/LexEBI/). The resource provides the disease terms as open

Multiple kernels learning-based biological entity relationship extraction method.

PubMed

Dongliang, Xu; Jingchang, Pan; Bailing, Wang

2017-09-20

Automatic extracting protein entity interaction information from biomedical literature can help to build protein relation network and design new drugs. There are more than 20 million literature abstracts included in MEDLINE, which is the most authoritative textual database in the field of biomedicine, and follow an exponential growth over time. This frantic expansion of the biomedical literature can often be difficult to absorb or manually analyze. Thus efficient and automated search engines are necessary to efficiently explore the biomedical literature using text mining techniques. The P, R, and F value of tag graph method in Aimed corpus are 50.82, 69.76, and 58.61%, respectively. The P, R, and F value of tag graph kernel method in other four evaluation corpuses are 2-5% higher than that of all-paths graph kernel. And The P, R and F value of feature kernel and tag graph kernel fuse methods is 53.43, 71.62 and 61.30%, respectively. The P, R and F value of feature kernel and tag graph kernel fuse methods is 55.47, 70.29 and 60.37%, respectively. It indicated that the performance of the two kinds of kernel fusion methods is better than that of simple kernel. In comparison with the all-paths graph kernel method, the tag graph kernel method is superior in terms of overall performance. Experiments show that the performance of the multi-kernels method is better than that of the three separate single-kernel method and the dual-mutually fused kernel method used hereof in five corpus sets.

Playing biology's name game: identifying protein names in scientific text.

PubMed

Hanisch, Daniel; Fluck, Juliane; Mevissen, Heinz-Theodor; Zimmer, Ralf

2003-01-01

A growing body of work is devoted to the extraction of protein or gene interaction information from the scientific literature. Yet, the basis for most extraction algorithms, i.e. the specific and sensitive recognition of protein and gene names and their numerous synonyms, has not been adequately addressed. Here we describe the construction of a comprehensive general purpose name dictionary and an accompanying automatic curation procedure based on a simple token model of protein names. We designed an efficient search algorithm to analyze all abstracts in MEDLINE in a reasonable amount of time on standard computers. The parameters of our method are optimized using machine learning techniques. Used in conjunction, these ingredients lead to good search performance. A supplementary web page is available at http://cartan.gmd.de/ProMiner/.

Automatic information extraction from unstructured mammography reports using distributed semantics.

PubMed

Gupta, Anupama; Banerjee, Imon; Rubin, Daniel L

2018-02-01

To date, the methods developed for automated extraction of information from radiology reports are mainly rule-based or dictionary-based, and, therefore, require substantial manual effort to build these systems. Recent efforts to develop automated systems for entity detection have been undertaken, but little work has been done to automatically extract relations and their associated named entities in narrative radiology reports that have comparable accuracy to rule-based methods. Our goal is to extract relations in a unsupervised way from radiology reports without specifying prior domain knowledge. We propose a hybrid approach for information extraction that combines dependency-based parse tree with distributed semantics for generating structured information frames about particular findings/abnormalities from the free-text mammography reports. The proposed IE system obtains a F 1 -score of 0.94 in terms of completeness of the content in the information frames, which outperforms a state-of-the-art rule-based system in this domain by a significant margin. The proposed system can be leveraged in a variety of applications, such as decision support and information retrieval, and may also easily scale to other radiology domains, since there is no need to tune the system with hand-crafted information extraction rules. Copyright © 2018 Elsevier Inc. All rights reserved.

Crataegus tanacetifolia leaf extract prevents L-NAME-induced hypertension in rats: a morphological study.

PubMed

Koçyildiz, Z Celebi; Birman, H; Olgaç, V; Akgün-Dar, K; Melikoğlu, G; Meriçli, A H

2006-01-01

Crataegus (hawthorn) has long been used as a folk medicine all around the world. Most of the studies with Crataegus species focus on effects on heart failure and cardiovascular disease. The pharmacological effects of Crataegus have been attributed mainly to the content of flavonoids, procyanidin, aromatic acid and cardiotonic amines. The present study investigated the blood pressure and the structure of the coronary arterial wall of L-NAME-induced hypertensive rats given an aqueous leaf extract of C. tanacetifolia (100 mg/kg), for 4 weeks via gavage. It was observed that C. tanacetifolia, especially the hyperoside fraction, prevented L-NAME-induced hypertension in rats and had beneficial effects on the cardiovascular system. Copyright 2006 John Wiley & Sons, Ltd.

Efficient Execution Methods of Pivoting for Bulk Extraction of Entity-Attribute-Value-Modeled Data

PubMed Central

Luo, Gang; Frey, Lewis J.

2017-01-01

Entity-attribute-value (EAV) tables are widely used to store data in electronic medical records and clinical study data management systems. Before they can be used by various analytical (e.g., data mining and machine learning) programs, EAV-modeled data usually must be transformed into conventional relational table format through pivot operations. This time-consuming and resource-intensive process is often performed repeatedly on a regular basis, e.g., to provide a daily refresh of the content in a clinical data warehouse. Thus, it would be beneficial to make pivot operations as efficient as possible. In this paper, we present three techniques for improving the efficiency of pivot operations: 1) filtering out EAV tuples related to unneeded clinical parameters early on; 2) supporting pivoting across multiple EAV tables; and 3) conducting multi-query optimization. We demonstrate the effectiveness of our techniques through implementation. We show that our optimized execution method of pivoting using these techniques significantly outperforms the current basic execution method of pivoting. Our techniques can be used to build a data extraction tool to simplify the specification of and improve the efficiency of extracting data from the EAV tables in electronic medical records and clinical study data management systems. PMID:25608318

2 CFR 170.110 - Types of entities to which this part applies.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 2 Grants and Agreements 1 2013-01-01 2013-01-01 false Types of entities to which this part applies...) Apply for or receive agency awards; or (2) Receive subawards under those awards. (b) Exceptions. (1... his or her name). (2) None of the requirements regarding reporting names and total compensation of an...

2 CFR 170.110 - Types of entities to which this part applies.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 2 Grants and Agreements 1 2012-01-01 2012-01-01 false Types of entities to which this part applies...) Apply for or receive agency awards; or (2) Receive subawards under those awards. (b) Exceptions. (1... his or her name). (2) None of the requirements regarding reporting names and total compensation of an...

High-recall protein entity recognition using a dictionary

PubMed Central

Kou, Zhenzhen; Cohen, William W.; Murphy, Robert F.

2010-01-01

Protein name extraction is an important step in mining biological literature. We describe two new methods for this task: semiCRFs and dictionary HMMs. SemiCRFs are a recently-proposed extension to conditional random fields that enables more effective use of dictionary information as features. Dictionary HMMs are a technique in which a dictionary is converted to a large HMM that recognizes phrases from the dictionary, as well as variations of these phrases. Standard training methods for HMMs can be used to learn which variants should be recognized. We compared the performance of our new approaches to that of Maximum Entropy (Max-Ent) and normal CRFs on three datasets, and improvement was obtained for all four methods over the best published results for two of the datasets. CRFs and semiCRFs achieved the highest overall performance according to the widely-used F-measure, while the dictionary HMMs performed the best at finding entities that actually appear in the dictionary—the measure of most interest in our intended application. PMID:15961466

12 CFR 602.24 - Responses to demands served on non-FCA employees or entities.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 12 Banks and Banking 6 2010-01-01 2010-01-01 false Responses to demands served on non-FCA employees or entities. 602.24 Section 602.24 Banks and Banking FARM CREDIT ADMINISTRATION ADMINISTRATIVE... Not a Named Party § 602.24 Responses to demands served on non-FCA employees or entities. If you are...

Protective effects of long-term administration of Ziziphus jujuba fruit extract on cardiovascular responses in L-NAME hypertensive rats.

PubMed

Mohebbati, Reza; Bavarsad, Kosar; Rahimi, Maryam; Rakhshandeh, Hasan; Khajavi Rad, Abolfazl; Shafei, Mohammad Naser

2018-01-01

Ziziphus jujuba stimulates the release of nitric oxide (NO). Because NO is involved in cardiovascular regulations, in this study the effects of hydroalcoholic extract of Z. jujuba on cardiovascular responses in acute NG-nitro-L-arginine methyl ester (L-NAME) hypertensive rats were evaluated. Rats were divided into 6 group (n=6): 1) saline, 2) L-NAME received (10mg/kg) intravenously, 3) sodium nitroprusside (SNP) (50µg/kg)+L-NAME group received SNP before L-NAME and 4-6) three groups of Z. jujuba (100, 200 and 400mg/kg) that treated for four weeks and on the 28 th day, L-NAME was injected. Femoral artery and vein were cannulated for recording cardiovascular responses and drug injection, respectively. Systolic blood pressure (SBP), Mean arterial pressure (MAP) and heart rate (HR) were recorded continuously. Maximal changes (∆) of SBP, MAP and HR were calculated and compared to control and L-NAME groups. In L-NAME group, maximal ΔSBP (L-NAME: 44.15±4.0 mmHg vs control: 0.71±2.1 mmHg) and ΔMAP (L-NAME: 40.8±4.0 mmHg vs control: 0.57±1.6 mmHg) significantly increased (p<0.001 in both) but ∆HR was not significant as compared to control (p>0.05). All doses of Z. jujuba attenuated maximal ∆SBP and ∆MAP induced by L-NAME but only the lowest dose (100 mg/kg) had significant effects (ΔSBP: 20.36±5.6 mmHg vs L-NAME: 44.1±4.0 mmHg and ΔMAP: 20.8±4.5 mmHg vs L-NAME: 40.8±3.8 mmHg (p<0.05 to p<0.01)). The ∆HR at three doses was not significantly different from that of L-NAME group (p>0.05). Because long-term consumption of Z. jujuba extract, especially its lowest dose, attenuated cardiovascular responses induced by L-NAME, we suggest that Z. jujuba has potential beneficial effects in prevention of hypertension induced by NO deficiency.

31 CFR 598.408 - Alleged change in ownership or control of an entity designated as a specially designated...

Code of Federal Regulations, 2010 CFR

2010-07-01

... of capital; and contracts evidencing the sale of the entity to its new owners. (b) Any continuing... narcotics trafficker could lead to designation of the purchaser. Mere change in name of an entity will not...

A sentence sliding window approach to extract protein annotations from biomedical articles

PubMed Central

Krallinger, Martin; Padron, Maria; Valencia, Alfonso

2005-01-01

Background Within the emerging field of text mining and statistical natural language processing (NLP) applied to biomedical articles, a broad variety of techniques have been developed during the past years. Nevertheless, there is still a great ned of comparative assessment of the performance of the proposed methods and the development of common evaluation criteria. This issue was addressed by the Critical Assessment of Text Mining Methods in Molecular Biology (BioCreative) contest. The aim of this contest was to assess the performance of text mining systems applied to biomedical texts including tools which recognize named entities such as genes and proteins, and tools which automatically extract protein annotations. Results The "sentence sliding window" approach proposed here was found to efficiently extract text fragments from full text articles containing annotations on proteins, providing the highest number of correctly predicted annotations. Moreover, the number of correct extractions of individual entities (i.e. proteins and GO terms) involved in the relationships used for the annotations was significantly higher than the correct extractions of the complete annotations (protein-function relations). Conclusion We explored the use of averaging sentence sliding windows for information extraction, especially in a context where conventional training data is unavailable. The combination of our approach with more refined statistical estimators and machine learning techniques might be a way to improve annotation extraction for future biomedical text mining applications. PMID:15960831

Geographic names of the Antarctic

USGS Publications Warehouse

,; ,; ,; ,; Alberts, Fred G.

1995-01-01

This gazetteer contains 12,710 names approved by the United States Board on Geographic Names and the Secretary of the Interior for features in Antarctica and the area extending northward to the Antarctic Convergence. Included in this geographic area, the Antarctic region, are the off-lying South Shetland Islands, the South Orkney Islands, the South Sandwich Islands, South Georgia, Bouvetøya, Heard Island, and the Balleny Islands. These names have been approved for use by U.S. Government agencies. Their use by the Antarctic specialist and the public is highly recommended for the sake of accuracy and uniformity. This publication, which supersedes previous Board gazetteers or lists for the area, contains names approved as recently as December 1994. The basic name coverage of this gazetteer corresponds to that of maps at the scale of 1:250,000 or larger for coastal Antarctica, the off-lying islands, and isolated mountains and ranges of the continent. Much of the interior of Antarctica is a featureless ice plateau. That area has been mapped at a smaller scale and is nearly devoid of toponyms. All of the names are for natural features, such as mountains, glaciers, peninsulas, capes, bays, islands, and subglacial entities. The names of scientific stations have not been listed alphabetically, but they may appear in the texts of some decisions. For the names of submarine features, reference should be made to the Gazetteer of Undersea Features, 4th edition, U.S. Board on Geographic Names, 1990.

Determining similarity of scientific entities in annotation datasets

PubMed Central

Palma, Guillermo; Vidal, Maria-Esther; Haag, Eric; Raschid, Louiqa; Thor, Andreas

2015-01-01

Linked Open Data initiatives have made available a diversity of scientific collections where scientists have annotated entities in the datasets with controlled vocabulary terms from ontologies. Annotations encode scientific knowledge, which is captured in annotation datasets. Determining relatedness between annotated entities becomes a building block for pattern mining, e.g. identifying drug–drug relationships may depend on the similarity of the targets that interact with each drug. A diversity of similarity measures has been proposed in the literature to compute relatedness between a pair of entities. Each measure exploits some knowledge including the name, function, relationships with other entities, taxonomic neighborhood and semantic knowledge. We propose a novel general-purpose annotation similarity measure called ‘AnnSim’ that measures the relatedness between two entities based on the similarity of their annotations. We model AnnSim as a 1–1 maximum weight bipartite match and exploit properties of existing solvers to provide an efficient solution. We empirically study the performance of AnnSim on real-world datasets of drugs and disease associations from clinical trials and relationships between drugs and (genomic) targets. Using baselines that include a variety of measures, we identify where AnnSim can provide a deeper understanding of the semantics underlying the relatedness of a pair of entities or where it could lead to predicting new links or identifying potential novel patterns. Although AnnSim does not exploit knowledge or properties of a particular domain, its performance compares well with a variety of state-of-the-art domain-specific measures. Database URL: http://www.yeastgenome.org/ PMID:25725057

Gene/protein name recognition based on support vector machine using dictionary as features.

PubMed

Mitsumori, Tomohiro; Fation, Sevrani; Murata, Masaki; Doi, Kouichi; Doi, Hirohumi

2005-01-01

Automated information extraction from biomedical literature is important because a vast amount of biomedical literature has been published. Recognition of the biomedical named entities is the first step in information extraction. We developed an automated recognition system based on the SVM algorithm and evaluated it in Task 1.A of BioCreAtIvE, a competition for automated gene/protein name recognition. In the work presented here, our recognition system uses the feature set of the word, the part-of-speech (POS), the orthography, the prefix, the suffix, and the preceding class. We call these features "internal resource features", i.e., features that can be found in the training data. Additionally, we consider the features of matching against dictionaries to be external resource features. We investigated and evaluated the effect of these features as well as the effect of tuning the parameters of the SVM algorithm. We found that the dictionary matching features contributed slightly to the improvement in the performance of the f-score. We attribute this to the possibility that the dictionary matching features might overlap with other features in the current multiple feature setting. During SVM learning, each feature alone had a marginally positive effect on system performance. This supports the fact that the SVM algorithm is robust on the high dimensionality of the feature vector space and means that feature selection is not required.

A study of active learning methods for named entity recognition in clinical text.

PubMed

Chen, Yukun; Lasko, Thomas A; Mei, Qiaozhu; Denny, Joshua C; Xu, Hua

2015-12-01

Named entity recognition (NER), a sequential labeling task, is one of the fundamental tasks for building clinical natural language processing (NLP) systems. Machine learning (ML) based approaches can achieve good performance, but they often require large amounts of annotated samples, which are expensive to build due to the requirement of domain experts in annotation. Active learning (AL), a sample selection approach integrated with supervised ML, aims to minimize the annotation cost while maximizing the performance of ML-based models. In this study, our goal was to develop and evaluate both existing and new AL methods for a clinical NER task to identify concepts of medical problems, treatments, and lab tests from the clinical notes. Using the annotated NER corpus from the 2010 i2b2/VA NLP challenge that contained 349 clinical documents with 20,423 unique sentences, we simulated AL experiments using a number of existing and novel algorithms in three different categories including uncertainty-based, diversity-based, and baseline sampling strategies. They were compared with the passive learning that uses random sampling. Learning curves that plot performance of the NER model against the estimated annotation cost (based on number of sentences or words in the training set) were generated to evaluate different active learning and the passive learning methods and the area under the learning curve (ALC) score was computed. Based on the learning curves of F-measure vs. number of sentences, uncertainty sampling algorithms outperformed all other methods in ALC. Most diversity-based methods also performed better than random sampling in ALC. To achieve an F-measure of 0.80, the best method based on uncertainty sampling could save 66% annotations in sentences, as compared to random sampling. For the learning curves of F-measure vs. number of words, uncertainty sampling methods again outperformed all other methods in ALC. To achieve 0.80 in F-measure, in comparison to random

Determining similarity of scientific entities in annotation datasets.

PubMed

Palma, Guillermo; Vidal, Maria-Esther; Haag, Eric; Raschid, Louiqa; Thor, Andreas

2015-01-01

Linked Open Data initiatives have made available a diversity of scientific collections where scientists have annotated entities in the datasets with controlled vocabulary terms from ontologies. Annotations encode scientific knowledge, which is captured in annotation datasets. Determining relatedness between annotated entities becomes a building block for pattern mining, e.g. identifying drug-drug relationships may depend on the similarity of the targets that interact with each drug. A diversity of similarity measures has been proposed in the literature to compute relatedness between a pair of entities. Each measure exploits some knowledge including the name, function, relationships with other entities, taxonomic neighborhood and semantic knowledge. We propose a novel general-purpose annotation similarity measure called 'AnnSim' that measures the relatedness between two entities based on the similarity of their annotations. We model AnnSim as a 1-1 maximum weight bipartite match and exploit properties of existing solvers to provide an efficient solution. We empirically study the performance of AnnSim on real-world datasets of drugs and disease associations from clinical trials and relationships between drugs and (genomic) targets. Using baselines that include a variety of measures, we identify where AnnSim can provide a deeper understanding of the semantics underlying the relatedness of a pair of entities or where it could lead to predicting new links or identifying potential novel patterns. Although AnnSim does not exploit knowledge or properties of a particular domain, its performance compares well with a variety of state-of-the-art domain-specific measures. Database URL: http://www.yeastgenome.org/ © The Author(s) 2015. Published by Oxford University Press.

Anti-hypertensive effects of the methanol/methylene chloride stem bark extract of Mammea africana in l-NAME-induced hypertensive rats.

PubMed

Nguelefack-Mbuyo, P E; Nguelefack, T B; Dongmo, A B; Afkir, S; Azebaze, A G B; Dimo, T; Legssyer, A; Kamanyi, A; Ziyyat, A

2008-05-22

The methanol/methylene chloride (CH(3)OH/CH(2)Cl(2)) extract from the stem bark of Mammea africana was showed to possess vasodilating effect in the presence and the absence of N(omega)-nitro-l-arginine methyl ester (l-NAME). The present study was designed to evaluate the effects of the methanol/methylene chloride from the stem bark of Mammea africana. The extract (200 mg/(kg day)) was administered orally in rats treated concurrently with l-NAME (40 mg/(kg day)). l-Arginine (100 mg/(kg day)) and captopril (20 mg/(kg day))were used as positive controls. Bodyweight, systolic arterial blood pressure and heart rate were measured weekly throughout the experiment period (28 days). At the end of treatment, animals were killed and the cardiac mass index evaluated. The aorta was used to evaluate the endothelium-dependant relaxation to carbachol. The aorta contraction induced by noradrenalin was also examined and expressed as a percentage of that induced by KCl. The extract neither affected the body weight nor the heart rate. The extract as captopril completely prevented the development of arterial hypertension. Both the substances failed to restore the endothelium-dependent vascular relaxation and increased the vascular contraction to norepinephrine in relation to KCl contraction. They also significantly reduced the left ventricular hypertrophy induced by l-NAME. These findings are in agreement with the traditional use of Mammea africana in the treatment of arterial hypertension and indicate that it may have a beneficial effect in patients with NO deficiency but will be unable to improve their endothelium-dependent vasorelaxation.

CD-REST: a system for extracting chemical-induced disease relation in literature.

PubMed

Xu, Jun; Wu, Yonghui; Zhang, Yaoyun; Wang, Jingqi; Lee, Hee-Jin; Xu, Hua

2016-01-01

Mining chemical-induced disease relations embedded in the vast biomedical literature could facilitate a wide range of computational biomedical applications, such as pharmacovigilance. The BioCreative V organized a Chemical Disease Relation (CDR) Track regarding chemical-induced disease relation extraction from biomedical literature in 2015. We participated in all subtasks of this challenge. In this article, we present our participation system Chemical Disease Relation Extraction SysTem (CD-REST), an end-to-end system for extracting chemical-induced disease relations in biomedical literature. CD-REST consists of two main components: (1) a chemical and disease named entity recognition and normalization module, which employs the Conditional Random Fields algorithm for entity recognition and a Vector Space Model-based approach for normalization; and (2) a relation extraction module that classifies both sentence-level and document-level candidate drug-disease pairs by support vector machines. Our system achieved the best performance on the chemical-induced disease relation extraction subtask in the BioCreative V CDR Track, demonstrating the effectiveness of our proposed machine learning-based approaches for automatic extraction of chemical-induced disease relations in biomedical literature. The CD-REST system provides web services using HTTP POST request. The web services can be accessed fromhttp://clinicalnlptool.com/cdr The online CD-REST demonstration system is available athttp://clinicalnlptool.com/cdr/cdr.html. Database URL:http://clinicalnlptool.com/cdr;http://clinicalnlptool.com/cdr/cdr.html. © The Author(s) 2016. Published by Oxford University Press.

Sortal anaphora resolution to enhance relation extraction from biomedical literature.

PubMed

Kilicoglu, Halil; Rosemblat, Graciela; Fiszman, Marcelo; Rindflesch, Thomas C

2016-04-14

Entity coreference is common in biomedical literature and it can affect text understanding systems that rely on accurate identification of named entities, such as relation extraction and automatic summarization. Coreference resolution is a foundational yet challenging natural language processing task which, if performed successfully, is likely to enhance such systems significantly. In this paper, we propose a semantically oriented, rule-based method to resolve sortal anaphora, a specific type of coreference that forms the majority of coreference instances in biomedical literature. The method addresses all entity types and relies on linguistic components of SemRep, a broad-coverage biomedical relation extraction system. It has been incorporated into SemRep, extending its core semantic interpretation capability from sentence level to discourse level. We evaluated our sortal anaphora resolution method in several ways. The first evaluation specifically focused on sortal anaphora relations. Our methodology achieved a F1 score of 59.6 on the test portion of a manually annotated corpus of 320 Medline abstracts, a 4-fold improvement over the baseline method. Investigating the impact of sortal anaphora resolution on relation extraction, we found that the overall effect was positive, with 50 % of the changes involving uninformative relations being replaced by more specific and informative ones, while 35 % of the changes had no effect, and only 15 % were negative. We estimate that anaphora resolution results in changes in about 1.5 % of approximately 82 million semantic relations extracted from the entire PubMed. Our results demonstrate that a heavily semantic approach to sortal anaphora resolution is largely effective for biomedical literature. Our evaluation and error analysis highlight some areas for further improvements, such as coordination processing and intra-sentential antecedent selection.

Naming, labeling, and packaging of pharmaceuticals.

PubMed

Kenagy, J W; Stein, G C

2001-11-01

The problem of medical errors associated with the naming, labeling, and packaging of pharmaceuticals is discussed. Sound-alike and look-alike drug names and packages can lead pharmacists and nurses to unintended interchanges of drugs that can result in patient injury or death. The existing medication-use system is flawed because its safety depends on human perfection. Simplicity, standardization, differentiation, lack of duplication, and unambiguous communication are human factors concepts that are relevant to the medication-use process. These principles have often been ignored in drug naming, labeling, and packaging. Instead, current methods are based on long-standing commercial considerations and bureaucratic procedures. The process for naming a marketable drug is lengthy and complex and involves submission of a new chemical entity and patent application, generic naming, brand naming, FDA review, and final approval. Drug companies seek the fastest possible approval and may believe that the incremental benefit of human factors evaluation is small. "Trade dress" is the concept that underlies labeling and packaging issues for the drug industry. Drug companies are resistant to changing trade dress and brand names. Although a variety of private-sector organizations have called for reforms in drug naming, labeling, and packaging standards have been proposed, the problem remains. Drug names, labels, and packages are not selected and designed in accordance with human factors principles. FDA standards do not require application of these principles, the drug industry has struggled with change, and private-sector initiatives have had only limited success.

Knowledge environments representing molecular entities for the virtual physiological human.

PubMed

Hofmann-Apitius, Martin; Fluck, Juliane; Furlong, Laura; Fornes, Oriol; Kolárik, Corinna; Hanser, Susanne; Boeker, Martin; Schulz, Stefan; Sanz, Ferran; Klinger, Roman; Mevissen, Theo; Gattermayer, Tobias; Oliva, Baldo; Friedrich, Christoph M

2008-09-13

In essence, the virtual physiological human (VPH) is a multiscale representation of human physiology spanning from the molecular level via cellular processes and multicellular organization of tissues to complex organ function. The different scales of the VPH deal with different entities, relationships and processes, and in consequence the models used to describe and simulate biological functions vary significantly. Here, we describe methods and strategies to generate knowledge environments representing molecular entities that can be used for modelling the molecular scale of the VPH. Our strategy to generate knowledge environments representing molecular entities is based on the combination of information extraction from scientific text and the integration of information from biomolecular databases. We introduce @neuLink, a first prototype of an automatically generated, disease-specific knowledge environment combining biomolecular, chemical, genetic and medical information. Finally, we provide a perspective for the future implementation and use of knowledge environments representing molecular entities for the VPH.

Unsupervised Medical Entity Recognition and Linking in Chinese Online Medical Text

PubMed Central

Gan, Liang; Cheng, Mian; Wu, Quanyuan

2018-01-01

Online medical text is full of references to medical entities (MEs), which are valuable in many applications, including medical knowledge-based (KB) construction, decision support systems, and the treatment of diseases. However, the diverse and ambiguous nature of the surface forms gives rise to a great difficulty for ME identification. Many existing solutions have focused on supervised approaches, which are often task-dependent. In other words, applying them to different kinds of corpora or identifying new entity categories requires major effort in data annotation and feature definition. In this paper, we propose unMERL, an unsupervised framework for recognizing and linking medical entities mentioned in Chinese online medical text. For ME recognition, unMERL first exploits a knowledge-driven approach to extract candidate entities from free text. Then, the categories of the candidate entities are determined using a distributed semantic-based approach. For ME linking, we propose a collaborative inference approach which takes full advantage of heterogenous entity knowledge and unstructured information in KB. Experimental results on real corpora demonstrate significant benefits compared to recent approaches with respect to both ME recognition and linking. PMID:29849994

"New drug" designations for new therapeutic entities: new active substance, new chemical entity, new biological entity, new molecular entity.

PubMed

Branch, Sarah K; Agranat, Israel

2014-11-13

This Perspective addresses ambiguities in designations of "new drugs" intended as new therapeutic entities (NTEs). Designation of an NTE as a new drug is significant, as it may confer regulatory exclusivity, an important incentive for development of novel compounds. Such designations differ between jurisdictions according to their drug laws and drug regulations. Chemical, biological, and innovative drugs are addressed in turn. The terms new chemical entity (NCE), new molecular entity (NME), new active substance (NAS), and new biological entity (NBE) as applied in worldwide jurisdictions are clarified. Differences between them are explored through case studies showing why new drugs have different periods of exclusivity in different jurisdictions or none at all. Finally, this Perspective recommends that in future, for the purpose of new drug compilations, NME is used for a new chemical drug, NBE for a new biological drug, and the combined designation NTE should refer to either an NME or an NBE.

Synergistic Antihypertensive Effect of Carthamus tinctorius L. Extract and Captopril in l-NAME-Induced Hypertensive Rats via Restoration of eNOS and AT1R Expression

PubMed Central

Maneesai, Putcharawipa; Prasarttong, Patoomporn; Bunbupha, Sarawoot; Kukongviriyapan, Upa; Kukongviriyapan, Veerapol; Tangsucharit, Panot; Prachaney, Parichat; Pakdeechote, Poungrat

2016-01-01

This study examined the effect of Carthamus tinctorius (CT) extract plus captopril treatment on blood pressure, vascular function, nitric oxide (NO) bioavailability, oxidative stress and renin-angiotensin system (RAS) in Nω-Nitro-l-arginine methyl ester (l-NAME)-induced hypertension. Rats were treated with l-NAME (40 mg/kg/day) for five weeks and given CT extract (75 or 150 or 300 or 500 mg/kg/day): captopril (5 mg/kg/day) or CT extract (300 mg/kg/day) plus captopril (5 mg/kg/day) for two consecutive weeks. CT extract reduced blood pressure dose-dependently, and the most effective dose was 300 mg/kg/day. l-NAME-induced hypertensive rats showed abnormalities including high blood pressure, high vascular resistance, impairment of acetylcholine-induced vasorelaxation in isolated aortic rings and mesenteric vascular beds, increased vascular superoxide production and plasma malondialdehyde levels, downregulation of eNOS, low level of plasma nitric oxide metabolites, upregulation of angiotensin II type 1 receptor and increased plasma angiotensin II. These abnormalities were alleviated by treatment with either CT extract or captopril. Combination treatment of CT extract and captopril normalized all the abnormalities found in hypertensive rats except endothelial dysfunction. These data indicate that there are synergistic antihypertensive effects of CT extract and captopril. These effects are likely mediated by their anti-oxidative properties and their inhibition of RAS. PMID:26938552

Abstracts versus Full Texts and Patents: A Quantitative Analysis of Biomedical Entities

NASA Astrophysics Data System (ADS)

Müller, Bernd; Klinger, Roman; Gurulingappa, Harsha; Mevissen, Heinz-Theodor; Hofmann-Apitius, Martin; Fluck, Juliane; Friedrich, Christoph M.

In information retrieval, named entity recognition gives the opportunity to apply semantic search in domain specific corpora. Recently, more full text patents and journal articles became freely available. As the information distribution amongst the different sections is unknown, an analysis of the diversity is of interest.

Unsupervised Extraction of Diagnosis Codes from EMRs Using Knowledge-Based and Extractive Text Summarization Techniques

PubMed Central

Kavuluru, Ramakanth; Han, Sifei; Harris, Daniel

2017-01-01

Diagnosis codes are extracted from medical records for billing and reimbursement and for secondary uses such as quality control and cohort identification. In the US, these codes come from the standard terminology ICD-9-CM derived from the international classification of diseases (ICD). ICD-9 codes are generally extracted by trained human coders by reading all artifacts available in a patient’s medical record following specific coding guidelines. To assist coders in this manual process, this paper proposes an unsupervised ensemble approach to automatically extract ICD-9 diagnosis codes from textual narratives included in electronic medical records (EMRs). Earlier attempts on automatic extraction focused on individual documents such as radiology reports and discharge summaries. Here we use a more realistic dataset and extract ICD-9 codes from EMRs of 1000 inpatient visits at the University of Kentucky Medical Center. Using named entity recognition (NER), graph-based concept-mapping of medical concepts, and extractive text summarization techniques, we achieve an example based average recall of 0.42 with average precision 0.47; compared with a baseline of using only NER, we notice a 12% improvement in recall with the graph-based approach and a 7% improvement in precision using the extractive text summarization approach. Although diagnosis codes are complex concepts often expressed in text with significant long range non-local dependencies, our present work shows the potential of unsupervised methods in extracting a portion of codes. As such, our findings are especially relevant for code extraction tasks where obtaining large amounts of training data is difficult. PMID:28748227

Extracting and standardizing medication information in clinical text - the MedEx-UIMA system.

PubMed

Jiang, Min; Wu, Yonghui; Shah, Anushi; Priyanka, Priyanka; Denny, Joshua C; Xu, Hua

2014-01-01

Extraction of medication information embedded in clinical text is important for research using electronic health records (EHRs). However, most of current medication information extraction systems identify drug and signature entities without mapping them to standard representation. In this study, we introduced the open source Java implementation of MedEx, an existing high-performance medication information extraction system, based on the Unstructured Information Management Architecture (UIMA) framework. In addition, we developed new encoding modules in the MedEx-UIMA system, which mapped an extracted drug name/dose/form to both generalized and specific RxNorm concepts and translated drug frequency information to ISO standard. We processed 826 documents by both systems and verified that MedEx-UIMA and MedEx (the Python version) performed similarly by comparing both results. Using two manually annotated test sets that contained 300 drug entries from medication list and 300 drug entries from narrative reports, the MedEx-UIMA system achieved F-measures of 98.5% and 97.5% respectively for encoding drug names to corresponding RxNorm generic drug ingredients, and F-measures of 85.4% and 88.1% respectively for mapping drug names/dose/form to the most specific RxNorm concepts. It also achieved an F-measure of 90.4% for normalizing frequency information to ISO standard. The open source MedEx-UIMA system is freely available online at http://code.google.com/p/medex-uima/.

Extraction of Pharmacokinetic Evidence of Drug–Drug Interactions from the Literature

PubMed Central

Kolchinsky, Artemy; Lourenço, Anália; Wu, Heng-Yi; Li, Lang; Rocha, Luis M.

2015-01-01

Drug-drug interaction (DDI) is a major cause of morbidity and mortality and a subject of intense scientific interest. Biomedical literature mining can aid DDI research by extracting evidence for large numbers of potential interactions from published literature and clinical databases. Though DDI is investigated in domains ranging in scale from intracellular biochemistry to human populations, literature mining has not been used to extract specific types of experimental evidence, which are reported differently for distinct experimental goals. We focus on pharmacokinetic evidence for DDI, essential for identifying causal mechanisms of putative interactions and as input for further pharmacological and pharmacoepidemiology investigations. We used manually curated corpora of PubMed abstracts and annotated sentences to evaluate the efficacy of literature mining on two tasks: first, identifying PubMed abstracts containing pharmacokinetic evidence of DDIs; second, extracting sentences containing such evidence from abstracts. We implemented a text mining pipeline and evaluated it using several linear classifiers and a variety of feature transforms. The most important textual features in the abstract and sentence classification tasks were analyzed. We also investigated the performance benefits of using features derived from PubMed metadata fields, various publicly available named entity recognizers, and pharmacokinetic dictionaries. Several classifiers performed very well in distinguishing relevant and irrelevant abstracts (reaching F1≈0.93, MCC≈0.74, iAUC≈0.99) and sentences (F1≈0.76, MCC≈0.65, iAUC≈0.83). We found that word bigram features were important for achieving optimal classifier performance and that features derived from Medical Subject Headings (MeSH) terms significantly improved abstract classification. We also found that some drug-related named entity recognition tools and dictionaries led to slight but significant improvements, especially in

Extracting and standardizing medication information in clinical text – the MedEx-UIMA system

PubMed Central

Jiang, Min; Wu, Yonghui; Shah, Anushi; Priyanka, Priyanka; Denny, Joshua C.; Xu, Hua

2014-01-01

Extraction of medication information embedded in clinical text is important for research using electronic health records (EHRs). However, most of current medication information extraction systems identify drug and signature entities without mapping them to standard representation. In this study, we introduced the open source Java implementation of MedEx, an existing high-performance medication information extraction system, based on the Unstructured Information Management Architecture (UIMA) framework. In addition, we developed new encoding modules in the MedEx-UIMA system, which mapped an extracted drug name/dose/form to both generalized and specific RxNorm concepts and translated drug frequency information to ISO standard. We processed 826 documents by both systems and verified that MedEx-UIMA and MedEx (the Python version) performed similarly by comparing both results. Using two manually annotated test sets that contained 300 drug entries from medication list and 300 drug entries from narrative reports, the MedEx-UIMA system achieved F-measures of 98.5% and 97.5% respectively for encoding drug names to corresponding RxNorm generic drug ingredients, and F-measures of 85.4% and 88.1% respectively for mapping drug names/dose/form to the most specific RxNorm concepts. It also achieved an F-measure of 90.4% for normalizing frequency information to ISO standard. The open source MedEx-UIMA system is freely available online at http://code.google.com/p/medex-uima/. PMID:25954575

Electrophysiological correlates of forming memories for faces, names, and face-name associations.

PubMed

Guo, Chunyan; Voss, Joel L; Paller, Ken A

2005-02-01

The ability to put a name to a face is a vital aspect of human interaction, but many people find this extremely difficult, especially after being introduced to someone for the first time. Creating enduring associations between arbitrary stimuli in this manner is also a prime example of what patients with amnesia find most difficult. To help develop a better understanding of this type of memory, we sought to obtain measures of the neural events responsible for successfully forming a new face-name association. We used event-related potentials (ERPs) extracted from high-density scalp EEG recordings in order to compare (1) memory for faces, (2) memory for names, and (3) memory for face-name associations. Each visual face appeared simultaneously with a unique spoken name. Signals observed 200-800 ms after the onset of face-name pairs predicted subsequent memory for faces, names, or face-name associations. Difference potentials observed as a function of subsequent memory performance were not identical for these three memory tests, nor were potentials predicting associative memory equivalent to the sum of potentials predicting item memory, suggesting that different neural events at the time of encoding are relevant for these distinct aspects of remembering people.

NeuroNames: an ontology for the BrainInfo portal to neuroscience on the web.

PubMed

Bowden, Douglas M; Song, Evan; Kosheleva, Julia; Dubach, Mark F

2012-01-01

BrainInfo ( http://braininfo.org ) is a growing portal to neuroscientific information on the Web. It is indexed by NeuroNames, an ontology designed to compensate for ambiguities in neuroanatomical nomenclature. The 20-year old ontology continues to evolve toward the ideal of recognizing all names of neuroanatomical entities and accommodating all structural concepts about which neuroscientists communicate, including multiple concepts of entities for which neuroanatomists have yet to determine the best or 'true' conceptualization. To make the definitions of structural concepts unambiguous and terminologically consistent we created a 'default vocabulary' of unique structure names selected from existing terminology. We selected standard names by criteria designed to maximize practicality for use in verbal communication as well as computerized knowledge management. The ontology of NeuroNames accommodates synonyms and homonyms of the standard terms in many languages. It defines complex structures as models composed of primary structures, which are defined in unambiguous operational terms. NeuroNames currently relates more than 16,000 names in eight languages to some 2,500 neuroanatomical concepts. The ontology is maintained in a relational database with three core tables: Names, Concepts and Models. BrainInfo uses NeuroNames to index information by structure, to interpret users' queries and to clarify terminology on remote web pages. NeuroNames is a resource vocabulary of the NLM's Unified Medical Language System (UMLS, 2011) and the basis for the brain regions component of NIFSTD (NeuroLex, 2011). The current version has been downloaded to hundreds of laboratories for indexing data and linking to BrainInfo, which attracts some 400 visitors/day, downloading 2,000 pages/day.

Naming the newly found landforms on Venus

NASA Technical Reports Server (NTRS)

Batson, R. M.; Russell, J. F.

1991-01-01

The mapping of Venus is unique in the history of cartigraphy; never has so much territory been discovered and mapped in so short a period of time. Therefore, in the interest of international scientific communication, there is a unique urgency to the development of a system of names for surface features on Venus. The process began with the naming of features seen on radar images taken from Earth and continued through mapping expeditions of the U.S. and U.S.S.R. However, the Magellan Mission resolves features twenty-five times smaller than those mapped previously, and its radar data will cover an area nearly equivalent to that of the continents and the sea-floors of the Earth combined. The International Astronomical Union (IAU) was charged with the formal endorsement of names of features on the planets. Proposed names are collected, approved, and applied through the IAU Working Group for Planetary System Nomenclature (WGPSN) and its task groups, prior to IAU approval by the IAU General Assembly. Names approved by the WGPSN and its task groups, prior to final approval may be used on published maps and articles, provided that their provisional nature is stipulated. The IAU has established themes for the names to be used on each of the planets; names of historical and mythological women are used on Venus. Names of political entities and those identified with active religions are not acceptable, and a person must have been deceased for three years or more to be considered. Any interested person may propose a name for consideration by the IAU.

Building an Entity-Centric Stream Filtering Test Collection for TREC 2012

DTIC Science & Technology

2012-11-01

spikes correspond to events, such as James McCartney suggesting that the sons of The Beatles form “The Beatles -- the next generation.” 4. KBA Task...gslis_adaptive, gslis_mult: Initial queries consist of wikitext extracted from each entitys history . We impose a document prior favoring docs with high in

78 FR 78514 - Designation of One Individual and Three Entities Pursuant to Executive Order

Federal Register 2010, 2011, 2012, 2013, 2014

2013-12-26

... DEPARTMENT OF THE TREASURY Office of Foreign Assets Control Designation of One Individual and... publishing the name of one individual and three entities whose property and interests in property are blocked..., Security, or Stability of Burma.'' DATES: The designation by the Director of OFAC of the one individual and...

Assessment of Orthographic Similarity of Drugs Names between Iran and Overseas Using the Solar Model

PubMed Central

ABOLHASSANI, Nazanin; AKBARI SARI, Ali; RASHIDIAN, Arash; RASTEGARPANAH, Mansoor

2017-01-01

Background: The recognition of patient safety is now occupying a prominent place on the health policy agenda since medical errors can result in adverse events. The existence of confusing drug names is one of the most common causes of medication errors. In Iran, the General Office of Trademarks Registry (GOTR), for four years (2010–2014) was responsible for approving drug proprietary names. This study aimed to investigate the performance of the GOTR in terms of drug names orthographic similarity using the SOLAR model. Methods: First, 100 names were randomly selected from the GOTR’s database. Then, each name was searched through pharmaceutical websites including Martindale (the Complete Drug Reference published by Pharmaceutical Press), Drugs.com and Medicines Complete. Pair of drugs whose names look orthographically similar with different indications were identified. Then, the SOLAR model was utilized to determine orthographic similarity between all pair of drug names. Results: The mean of match values of these 100 pairs of drug was 77% indicating the high risk of similarity. The match value for most of the reviewed pairs (92%) was high (≥66%). This value was medium (≥ 33% and <66%) just for 8% of the pairs of drug. These results indicate high risk of confusion due to similarity of drug names. Conclusion: The stewardship of the GOTR in patient safety considerations is fundamentally problematic. Thus, as a best practice, we recommend that proprietary names of drugs be evaluated by an entity within the health system. While an entity within the health system should address patient safety considerations, the GOTR is responsible for intellectual property rights. PMID:29259940

Relatedness-based Multi-Entity Summarization

PubMed Central

Gunaratna, Kalpa; Yazdavar, Amir Hossein; Thirunarayan, Krishnaprasad; Sheth, Amit; Cheng, Gong

2017-01-01

Representing world knowledge in a machine processable format is important as entities and their descriptions have fueled tremendous growth in knowledge-rich information processing platforms, services, and systems. Prominent applications of knowledge graphs include search engines (e.g., Google Search and Microsoft Bing), email clients (e.g., Gmail), and intelligent personal assistants (e.g., Google Now, Amazon Echo, and Apple’s Siri). In this paper, we present an approach that can summarize facts about a collection of entities by analyzing their relatedness in preference to summarizing each entity in isolation. Specifically, we generate informative entity summaries by selecting: (i) inter-entity facts that are similar and (ii) intra-entity facts that are important and diverse. We employ a constrained knapsack problem solving approach to efficiently compute entity summaries. We perform both qualitative and quantitative experiments and demonstrate that our approach yields promising results compared to two other stand-alone state-of-the-art entity summarization approaches. PMID:29051696

Detection of IUPAC and IUPAC-like chemical names.

PubMed

Klinger, Roman; Kolárik, Corinna; Fluck, Juliane; Hofmann-Apitius, Martin; Friedrich, Christoph M

2008-07-01

Chemical compounds like small signal molecules or other biological active chemical substances are an important entity class in life science publications and patents. Several representations and nomenclatures for chemicals like SMILES, InChI, IUPAC or trivial names exist. Only SMILES and InChI names allow a direct structure search, but in biomedical texts trivial names and Iupac like names are used more frequent. While trivial names can be found with a dictionary-based approach and in such a way mapped to their corresponding structures, it is not possible to enumerate all IUPAC names. In this work, we present a new machine learning approach based on conditional random fields (CRF) to find mentions of IUPAC and IUPAC-like names in scientific text as well as its evaluation and the conversion rate with available name-to-structure tools. We present an IUPAC name recognizer with an F(1) measure of 85.6% on a MEDLINE corpus. The evaluation of different CRF orders and offset conjunction orders demonstrates the importance of these parameters. An evaluation of hand-selected patent sections containing large enumerations and terms with mixed nomenclature shows a good performance on these cases (F(1) measure 81.5%). Remaining recognition problems are to detect correct borders of the typically long terms, especially when occurring in parentheses or enumerations. We demonstrate the scalability of our implementation by providing results from a full MEDLINE run. We plan to publish the corpora, annotation guideline as well as the conditional random field model as a UIMA component.

13 CFR 130.200 - Eligible entities.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 13 Business Credit and Assistance 1 2011-01-01 2011-01-01 false Eligible entities. 130.200 Section... CENTERS § 130.200 Eligible entities. (a) Recipient Organization. The following entities are eligible to... community or junior college; (5) An entity formed by two or more of the above entities; or (6) Any entity...

31 CFR 575.304 - Entity of the Government of Iraq; Iraqi Government entity.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false Entity of the Government of Iraq; Iraqi Government entity. 575.304 Section 575.304 Money and Finance: Treasury Regulations Relating to... SANCTIONS REGULATIONS General Definitions § 575.304 Entity of the Government of Iraq; Iraqi Government...

OrganismTagger: detection, normalization and grounding of organism entities in biomedical documents.

PubMed

Naderi, Nona; Kappler, Thomas; Baker, Christopher J O; Witte, René

2011-10-01

Semantic tagging of organism mentions in full-text articles is an important part of literature mining and semantic enrichment solutions. Tagged organism mentions also play a pivotal role in disambiguating other entities in a text, such as proteins. A high-precision organism tagging system must be able to detect the numerous forms of organism mentions, including common names as well as the traditional taxonomic groups: genus, species and strains. In addition, such a system must resolve abbreviations and acronyms, assign the scientific name and if possible link the detected mention to the NCBI Taxonomy database for further semantic queries and literature navigation. We present the OrganismTagger, a hybrid rule-based/machine learning system to extract organism mentions from the literature. It includes tools for automatically generating lexical and ontological resources from a copy of the NCBI Taxonomy database, thereby facilitating system updates by end users. Its novel ontology-based resources can also be reused in other semantic mining and linked data tasks. Each detected organism mention is normalized to a canonical name through the resolution of acronyms and abbreviations and subsequently grounded with an NCBI Taxonomy database ID. In particular, our system combines a novel machine-learning approach with rule-based and lexical methods for detecting strain mentions in documents. On our manually annotated OT corpus, the OrganismTagger achieves a precision of 95%, a recall of 94% and a grounding accuracy of 97.5%. On the manually annotated corpus of Linnaeus-100, the results show a precision of 99%, recall of 97% and grounding accuracy of 97.4%. The OrganismTagger, including supporting tools, resources, training data and manual annotations, as well as end user and developer documentation, is freely available under an open-source license at http://www.semanticsoftware.info/organism-tagger. witte@semanticsoftware.info.

Induced lexico-syntactic patterns improve information extraction from online medical forums.

PubMed

Gupta, Sonal; MacLean, Diana L; Heer, Jeffrey; Manning, Christopher D

2014-01-01

To reliably extract two entity types, symptoms and conditions (SCs), and drugs and treatments (DTs), from patient-authored text (PAT) by learning lexico-syntactic patterns from data annotated with seed dictionaries. Despite the increasing quantity of PAT (eg, online discussion threads), tools for identifying medical entities in PAT are limited. When applied to PAT, existing tools either fail to identify specific entity types or perform poorly. Identification of SC and DT terms in PAT would enable exploration of efficacy and side effects for not only pharmaceutical drugs, but also for home remedies and components of daily care. We use SC and DT term dictionaries compiled from online sources to label several discussion forums from MedHelp (http://www.medhelp.org). We then iteratively induce lexico-syntactic patterns corresponding strongly to each entity type to extract new SC and DT terms. Our system is able to extract symptom descriptions and treatments absent from our original dictionaries, such as 'LADA', 'stabbing pain', and 'cinnamon pills'. Our system extracts DT terms with 58-70% F1 score and SC terms with 66-76% F1 score on two forums from MedHelp. We show improvements over MetaMap, OBA, a conditional random field-based classifier, and a previous pattern learning approach. Our entity extractor based on lexico-syntactic patterns is a successful and preferable technique for identifying specific entity types in PAT. To the best of our knowledge, this is the first paper to extract SC and DT entities from PAT. We exhibit learning of informal terms often used in PAT but missing from typical dictionaries. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

31 CFR 800.211 - Entity.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 31 Money and Finance:Treasury 3 2011-07-01 2011-07-01 false Entity. 800.211 Section 800.211 Money... FOREIGN PERSONS Definitions § 800.211 Entity. The term entity means any branch, partnership, group or sub... separate legal entity) operated by any one of the foregoing as a business undertaking in a particular...

Extraction of CYP chemical interactions from biomedical literature using natural language processing methods.

PubMed

Jiao, Dazhi; Wild, David J

2009-02-01

This paper proposes a system that automatically extracts CYP protein and chemical interactions from journal article abstracts, using natural language processing (NLP) and text mining methods. In our system, we employ a maximum entropy based learning method, using results from syntactic, semantic, and lexical analysis of texts. We first present our system architecture and then discuss the data set for training our machine learning based models and the methods in building components in our system, such as part of speech (POS) tagging, Named Entity Recognition (NER), dependency parsing, and relation extraction. An evaluation of the system is conducted at the end, yielding very promising results: The POS, dependency parsing, and NER components in our system have achieved a very high level of accuracy as measured by precision, ranging from 85.9% to 98.5%, and the precision and the recall of the interaction extraction component are 76.0% and 82.6%, and for the overall system are 68.4% and 72.2%, respectively.

Exploring the Repeated Name Penalty and the Overt Pronoun Penalty in Spanish

ERIC Educational Resources Information Center

Gelormini-Lezama, Carlos

2018-01-01

Anaphoric expressions such as repeated names, overt pronouns, and null pronouns serve a major role in the creation and maintenance of discourse coherence. The felicitous use of an anaphoric expression is highly dependent on the discourse salience of the entity introduced by the antecedent. Gordon et al. ("Cogn Sci" 17:311-347, 1993)…

78 FR 37664 - Identification of Entities Pursuant to the Iranian Transactions and Sanctions Regulations and...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-06-21

... Control (``OFAC'') is publishing the names of 38 entities identified as the Government of Iran under the... Government of Iran and Iranian Financial Institutions'' (the ``Order''). Section 1(a) of the Order blocks, with certain exceptions, all property and interests in property of the Government of Iran, including...

A database of natural products and chemical entities from marine habitat

PubMed Central

Babu, Padavala Ajay; Puppala, Suma Sree; Aswini, Satyavarapu Lakshmi; Vani, Metta Ramya; Kumar, Chinta Narasimha; Prasanna, Tallapragada

2008-01-01

Marine compound database consists of marine natural products and chemical entities, collected from various literature sources, which are known to possess bioactivity against human diseases. The database is constructed using html code. The 12 categories of 182 compounds are provided with the source, compound name, 2-dimensional structure, bioactivity and clinical trial information. The database is freely available online and can be accessed at http://www.progenebio.in/mcdb/index.htm PMID:19238254

Detection of IUPAC and IUPAC-like chemical names

PubMed Central

Klinger, Roman; Kolářik, Corinna; Fluck, Juliane; Hofmann-Apitius, Martin; Friedrich, Christoph M.

2008-01-01

Motivation: Chemical compounds like small signal molecules or other biological active chemical substances are an important entity class in life science publications and patents. Several representations and nomenclatures for chemicals like SMILES, InChI, IUPAC or trivial names exist. Only SMILES and InChI names allow a direct structure search, but in biomedical texts trivial names and Iupac like names are used more frequent. While trivial names can be found with a dictionary-based approach and in such a way mapped to their corresponding structures, it is not possible to enumerate all IUPAC names. In this work, we present a new machine learning approach based on conditional random fields (CRF) to find mentions of IUPAC and IUPAC-like names in scientific text as well as its evaluation and the conversion rate with available name-to-structure tools. Results: We present an IUPAC name recognizer with an F1 measure of 85.6% on a MEDLINE corpus. The evaluation of different CRF orders and offset conjunction orders demonstrates the importance of these parameters. An evaluation of hand-selected patent sections containing large enumerations and terms with mixed nomenclature shows a good performance on these cases (F1 measure 81.5%). Remaining recognition problems are to detect correct borders of the typically long terms, especially when occurring in parentheses or enumerations. We demonstrate the scalability of our implementation by providing results from a full MEDLINE run. Availability: We plan to publish the corpora, annotation guideline as well as the conditional random field model as a UIMA component. Contact: roman.klinger@scai.fraunhofer.de PMID:18586724

22 CFR 96.5 - Requirement that accrediting entity be a nonprofit or public entity.

Code of Federal Regulations, 2010 CFR

2010-04-01

... administering standards for entities providing child welfare services; or (b) A public entity (other than a... political subdivision, agency, or instrumentality thereof, that is responsible for licensing adoption agencies in a State and that has expertise in developing and administering standards for entities providing...

Extracting laboratory test information from biomedical text

PubMed Central

Kang, Yanna Shen; Kayaalp, Mehmet

2013-01-01

Background: No previous study reported the efficacy of current natural language processing (NLP) methods for extracting laboratory test information from narrative documents. This study investigates the pathology informatics question of how accurately such information can be extracted from text with the current tools and techniques, especially machine learning and symbolic NLP methods. The study data came from a text corpus maintained by the U.S. Food and Drug Administration, containing a rich set of information on laboratory tests and test devices. Methods: The authors developed a symbolic information extraction (SIE) system to extract device and test specific information about four types of laboratory test entities: Specimens, analytes, units of measures and detection limits. They compared the performance of SIE and three prominent machine learning based NLP systems, LingPipe, GATE and BANNER, each implementing a distinct supervised machine learning method, hidden Markov models, support vector machines and conditional random fields, respectively. Results: Machine learning systems recognized laboratory test entities with moderately high recall, but low precision rates. Their recall rates were relatively higher when the number of distinct entity values (e.g., the spectrum of specimens) was very limited or when lexical morphology of the entity was distinctive (as in units of measures), yet SIE outperformed them with statistically significant margins on extracting specimen, analyte and detection limit information in both precision and F-measure. Its high recall performance was statistically significant on analyte information extraction. Conclusions: Despite its shortcomings against machine learning methods, a well-tailored symbolic system may better discern relevancy among a pile of information of the same type and may outperform a machine learning system by tapping into lexically non-local contextual information such as the document structure. PMID:24083058

Naming and recognizing famous faces in temporal lobe epilepsy.

PubMed

Glosser, G; Salvucci, A E; Chiaravalloti, N D

2003-07-08

To assess naming and recognition of faces of familiar famous people in patients with epilepsy before and after anterior temporal lobectomy (ATL). Color photographs of famous people were presented for naming and description to 63 patients with temporal lobe epilepsy (TLE) either before or after ATL and to 10 healthy age- and education-matched controls. Spontaneous naming of photographed famous people was impaired in all patient groups, but was most abnormal in patients who had undergone left ATL. When allowed to demonstrate knowledge of the famous faces through verbal descriptions, rather than naming, patients with left TLE, left ATL, and right TLE improved to normal levels, but patients with right ATL were still impaired, suggesting a new deficit in identifying famous faces. Naming of famous people was related to naming of other common objects, verbal memory, and perceptual discrimination of faces. Recognition of the identity of pictured famous people was more related to visuospatial perception and memory. Lesions in anterior regions of the right temporal lobe impair recognition of the identities of familiar faces, as well as the learning of new faces. Lesions in the left temporal lobe, especially in anterior regions, disrupt access to the names of known people, but do not affect recognition of the identities of famous faces. Results are consistent with the hypothesized role of lateralized anterior temporal lobe structures in facial recognition and naming of unique entities.

Evaluating Stream Filtering for Entity Profile Updates in TREC 2012, 2013, and 2014 (KBA Track Overview, Notebook Paper)

DTIC Science & Technology

2014-11-01

possible future directions that build on the KBA experience.   Data Assets   In addition to the three hundred run submissions from diverse systems...form name of an entity and assigning a confidence score based on the number of matches of tokens in the name. See code in github [6]. macro-P...131 64 GENDER 4 2 FoundedBy     56 30 NAME 2 2 DateOfDeath     54 12 TOP_MEMBERS_EMPLOYEES 2 1 EmployeeOf     44 19 WON_AWARD 1 1

78 FR 38537 - Federal Acquisition Regulation; Federal Acquisition Circular 2005-68; Small Entity Compliance Guide

Federal Register 2010, 2011, 2012, 2013, 2014

2013-06-26

... Entity Compliance Guide. SUMMARY: This document is issued under the joint authority of DOD, GSA, and NASA... whose name appears in the table below. Please cite FAC 2005-68 and the FAR case number. For information... Listed in FAC 2005-68 Subject FAR Case Analyst *Expansion of Applicability of the Senior Executive...

The taxonomic name resolution service: an online tool for automated standardization of plant names

PubMed Central

2013-01-01

Background The digitization of biodiversity data is leading to the widespread application of taxon names that are superfluous, ambiguous or incorrect, resulting in mismatched records and inflated species numbers. The ultimate consequences of misspelled names and bad taxonomy are erroneous scientific conclusions and faulty policy decisions. The lack of tools for correcting this ‘names problem’ has become a fundamental obstacle to integrating disparate data sources and advancing the progress of biodiversity science. Results The TNRS, or Taxonomic Name Resolution Service, is an online application for automated and user-supervised standardization of plant scientific names. The TNRS builds upon and extends existing open-source applications for name parsing and fuzzy matching. Names are standardized against multiple reference taxonomies, including the Missouri Botanical Garden's Tropicos database. Capable of processing thousands of names in a single operation, the TNRS parses and corrects misspelled names and authorities, standardizes variant spellings, and converts nomenclatural synonyms to accepted names. Family names can be included to increase match accuracy and resolve many types of homonyms. Partial matching of higher taxa combined with extraction of annotations, accession numbers and morphospecies allows the TNRS to standardize taxonomy across a broad range of active and legacy datasets. Conclusions We show how the TNRS can resolve many forms of taxonomic semantic heterogeneity, correct spelling errors and eliminate spurious names. As a result, the TNRS can aid the integration of disparate biological datasets. Although the TNRS was developed to aid in standardizing plant names, its underlying algorithms and design can be extended to all organisms and nomenclatural codes. The TNRS is accessible via a web interface at http://tnrs.iplantcollaborative.org/ and as a RESTful web service and application programming interface. Source code is available at https

Wnt pathway curation using automated natural language processing: combining statistical methods with partial and full parse for knowledge extraction.

PubMed

Santos, Carlos; Eggle, Daniela; States, David J

2005-04-15

Wnt signaling is a very active area of research with highly relevant publications appearing at a rate of more than one per day. Building and maintaining databases describing signal transduction networks is a time-consuming and demanding task that requires careful literature analysis and extensive domain-specific knowledge. For instance, more than 50 factors involved in Wnt signal transduction have been identified as of late 2003. In this work we describe a natural language processing (NLP) system that is able to identify references to biological interaction networks in free text and automatically assembles a protein association and interaction map. A 'gold standard' set of names and assertions was derived by manual scanning of the Wnt genes website (http://www.stanford.edu/~rnusse/wntwindow.html) including 53 interactions involved in Wnt signaling. This system was used to analyze a corpus of peer-reviewed articles related to Wnt signaling including 3369 Pubmed and 1230 full text papers. Names for key Wnt-pathway associated proteins and biological entities are identified using a chi-squared analysis of noun phrases over-represented in the Wnt literature as compared to the general signal transduction literature. Interestingly, we identified several instances where generic terms were used on the website when more specific terms occur in the literature, and one typographic error on the Wnt canonical pathway. Using the named entity list and performing an exhaustive assertion extraction of the corpus, 34 of the 53 interactions in the 'gold standard' Wnt signaling set were successfully identified (64% recall). In addition, the automated extraction found several interactions involving key Wnt-related molecules which were missing or different from those in the canonical diagram, and these were confirmed by manual review of the text. These results suggest that a combination of NLP techniques for information extraction can form a useful first-pass tool for assisting human

31 CFR 575.303 - Entity.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false Entity. 575.303 Section 575.303 Money... CONTROL, DEPARTMENT OF THE TREASURY IRAQI SANCTIONS REGULATIONS General Definitions § 575.303 Entity. The term entity includes a corporation, partnership, association, or other organization. ...

Acquired bilateral telangiectatic macules: a distinct clinical entity.

PubMed

Park, Ji-Hye; Lee, Dong Jun; Lee, Yoo-Jung; Jang, Yong Hyun; Kang, Hee Young; Kim, You Chan

2014-09-01

We evaluated 13 distinct patients with multiple telangiectatic pigmented macules confined mostly to the upper arms to determine if the clinical and histopathological features of these cases might represent a specific clinical entity. We retrospectively investigated the clinical, histopathologic, and immunohistochemical features of 13 patients with multiple telangiectatic pigmented macules on the upper arms who presented between January 2003 and December 2012. Epidermal pigmentation, melanogenic activity, melanocyte number, vascularity, epidermal thickness, and perivascular mast cell number of the specimens were evaluated. Clinically, the condition favored middle-aged men. On histopathologic examination, the lesional skin showed capillary proliferation and telangiectasia in the upper dermis. Histochemical and immunohistochemical analysis revealed basal hyperpigmentation and increased melanogenic activity in the lesional skin (P < .05). No significant difference in epidermal thickness or mast cell number was observed between the normal perilesional skin and the lesional skin. The clinical and histopathologic features of these lesions were relatively consistent in all patients. In addition, the features are quite distinct from other diseases. Based on clinical and histologic features, we suggest the name acquired bilateral telangiectatic macules for this new entity.

7 CFR 1738.16 - Eligible entities.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 11 2010-01-01 2010-01-01 false Eligible entities. 1738.16 Section 1738.16... Eligible entities. (a) RUS makes broadband loans to legally organized entities providing, or proposing to provide, broadband services in eligible rural communities. (1) Types of eligible entities include...

7 CFR 1738.16 - Eligible entities.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 7 Agriculture 11 2011-01-01 2011-01-01 false Eligible entities. 1738.16 Section 1738.16... Eligible entities. (a) RUS makes broadband loans to legally organized entities providing, or proposing to provide, broadband services in eligible rural communities. (1) Types of eligible entities include...

7 CFR 63.4 - Eligible entity.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 7 Agriculture 3 2011-01-01 2011-01-01 false Eligible entity. 63.4 Section 63.4 Agriculture Regulations of the Department of Agriculture (Continued) AGRICULTURAL MARKETING SERVICE (Standards... IMPROVEMENT CENTER General Provisions Definitions § 63.4 Eligible entity. Eligible entity means an entity that...

31 CFR 551.303 - Entity.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 31 Money and Finance:Treasury 3 2011-07-01 2011-07-01 false Entity. 551.303 Section 551.303 Money... CONTROL, DEPARTMENT OF THE TREASURY SOMALIA SANCTIONS REGULATIONS General Definitions § 551.303 Entity. The term entity means a partnership, association, trust, joint venture, corporation, group, subgroup...

31 CFR 537.304 - Entity.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false Entity. 537.304 Section 537.304 Money... CONTROL, DEPARTMENT OF THE TREASURY BURMESE SANCTIONS REGULATIONS General Definitions § 537.304 Entity. The term entity means a partnership, association, trust, joint venture, corporation, group, subgroup...

31 CFR 548.303 - Entity.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false Entity. 548.303 Section 548.303 Money... CONTROL, DEPARTMENT OF THE TREASURY BELARUS SANCTIONS REGULATIONS General Definitions § 548.303 Entity. The term entity means a partnership, association, trust, joint venture, corporation, group, subgroup...

31 CFR 551.303 - Entity.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false Entity. 551.303 Section 551.303 Money... CONTROL, DEPARTMENT OF THE TREASURY SOMALIA SANCTIONS REGULATIONS General Definitions § 551.303 Entity. The term entity means a partnership, association, trust, joint venture, corporation, group, subgroup...

31 CFR 538.303 - Entity.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false Entity. 538.303 Section 538.303 Money... CONTROL, DEPARTMENT OF THE TREASURY SUDANESE SANCTIONS REGULATIONS General Definitions § 538.303 Entity. The term entity means a partnership, association, trust, joint venture, corporation, or other...

31 CFR 538.303 - Entity.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 31 Money and Finance:Treasury 3 2011-07-01 2011-07-01 false Entity. 538.303 Section 538.303 Money... CONTROL, DEPARTMENT OF THE TREASURY SUDANESE SANCTIONS REGULATIONS General Definitions § 538.303 Entity. The term entity means a partnership, association, trust, joint venture, corporation, or other...

31 CFR 536.303 - Entity.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 31 Money and Finance:Treasury 3 2011-07-01 2011-07-01 false Entity. 536.303 Section 536.303 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS....303 Entity. The term entity means a partnership, association, corporation, or other organization...

31 CFR 595.303 - Entity.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 31 Money and Finance:Treasury 3 2011-07-01 2011-07-01 false Entity. 595.303 Section 595.303 Money... CONTROL, DEPARTMENT OF THE TREASURY TERRORISM SANCTIONS REGULATIONS General Definitions § 595.303 Entity. The term entity means a partnership, association, corporation, or other organization, group or...

31 CFR 545.303 - Entity.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false Entity. 545.303 Section 545.303 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS....303 Entity. The term entity means a partnership, association, corporation, or other organization...

31 CFR 543.304 - Entity.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 31 Money and Finance:Treasury 3 2011-07-01 2011-07-01 false Entity. 543.304 Section 543.304 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS... Entity. The term entity means a partnership, association, trust, joint venture, corporation, group...

31 CFR 541.303 - Entity.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false Entity. 541.303 Section 541.303 Money... CONTROL, DEPARTMENT OF THE TREASURY ZIMBABWE SANCTIONS REGULATIONS General Definitions § 541.303 Entity. The term entity means a partnership, association, trust, joint venture, corporation, group, subgroup...

31 CFR 537.304 - Entity.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 31 Money and Finance:Treasury 3 2011-07-01 2011-07-01 false Entity. 537.304 Section 537.304 Money... CONTROL, DEPARTMENT OF THE TREASURY BURMESE SANCTIONS REGULATIONS General Definitions § 537.304 Entity. The term entity means a partnership, association, trust, joint venture, corporation, group, subgroup...

31 CFR 536.303 - Entity.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false Entity. 536.303 Section 536.303 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS....303 Entity. The term entity means a partnership, association, corporation, or other organization...

31 CFR 595.303 - Entity.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false Entity. 595.303 Section 595.303 Money... CONTROL, DEPARTMENT OF THE TREASURY TERRORISM SANCTIONS REGULATIONS General Definitions § 595.303 Entity. The term entity means a partnership, association, corporation, or other organization, group or...

31 CFR 543.304 - Entity.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false Entity. 543.304 Section 543.304 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS... Entity. The term entity means a partnership, association, trust, joint venture, corporation, group...

31 CFR 549.303 - Entity.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 31 Money and Finance:Treasury 3 2011-07-01 2011-07-01 false Entity. 549.303 Section 549.303 Money... CONTROL, DEPARTMENT OF THE TREASURY LEBANON SANCTIONS REGULATIONS General Definitions § 549.303 Entity. The term entity means a partnership, association, trust, joint venture, corporation, group, subgroup...

31 CFR 548.303 - Entity.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 31 Money and Finance:Treasury 3 2011-07-01 2011-07-01 false Entity. 548.303 Section 548.303 Money... CONTROL, DEPARTMENT OF THE TREASURY BELARUS SANCTIONS REGULATIONS General Definitions § 548.303 Entity. The term entity means a partnership, association, trust, joint venture, corporation, group, subgroup...

31 CFR 541.303 - Entity.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 31 Money and Finance:Treasury 3 2011-07-01 2011-07-01 false Entity. 541.303 Section 541.303 Money... CONTROL, DEPARTMENT OF THE TREASURY ZIMBABWE SANCTIONS REGULATIONS General Definitions § 541.303 Entity. The term entity means a partnership, association, trust, joint venture, corporation, group, subgroup...

47 CFR 90.1103 - Designated entities.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 47 Telecommunication 5 2010-10-01 2010-10-01 false Designated entities. 90.1103 Section 90.1103... Designated entities. (a) This section addresses certain issues concerning designated entities in the Location... provisions. (1) A small business is an entity that, together with its affiliates and controlling interests...

47 CFR 90.1103 - Designated entities.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 47 Telecommunication 5 2011-10-01 2011-10-01 false Designated entities. 90.1103 Section 90.1103... Designated entities. (a) This section addresses certain issues concerning designated entities in the Location... provisions. (1) A small business is an entity that, together with its affiliates and controlling interests...

31 CFR 596.308 - Person; entity.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false Person; entity. 596.308 Section 596... General Definitions § 596.308 Person; entity. (a) The term person means an individual or entity. (b) The term entity means a partnership, association, corporation, or other organization. ...

31 CFR 596.308 - Person; entity.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 31 Money and Finance:Treasury 3 2011-07-01 2011-07-01 false Person; entity. 596.308 Section 596... General Definitions § 596.308 Person; entity. (a) The term person means an individual or entity. (b) The term entity means a partnership, association, corporation, or other organization. ...

78 FR 80381 - Federal Acquisition Regulation; Federal Acquisition Circular 2005-72; Small Entity Compliance Guide

Federal Register 2010, 2011, 2012, 2013, 2014

2013-12-31

...: Small Entity Compliance Guide. SUMMARY: This document is issued under the joint authority of DOD, GSA..., contact the analyst whose name appears in the table below. Please cite FAC 2005-72 and the FAR case number... 202- 501-4755. Rules Listed in FAC 2005-72 Item Subject FAR Case Analyst *I Service 2010-010 Loeb...

31 CFR 544.303 - Entity.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false Entity. 544.303 Section 544.303 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS... General Definitions § 544.303 Entity. The term entity means a partnership, association, trust, joint...

31 CFR 594.303 - Entity.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false Entity. 594.303 Section 594.303 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS... Entity. The term entity means a partnership, association, corporation, or other organization, group, or...

31 CFR 594.303 - Entity.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 31 Money and Finance:Treasury 3 2011-07-01 2011-07-01 false Entity. 594.303 Section 594.303 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS... Entity. The term entity means a partnership, association, corporation, or other organization, group, or...

31 CFR 598.303 - Entity.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false Entity. 598.303 Section 598.303 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS... § 598.303 Entity. The term entity means a partnership, joint venture, association, corporation...

31 CFR 542.303 - Entity.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false Entity. 542.303 Section 542.303 Money... CONTROL, DEPARTMENT OF THE TREASURY SYRIAN SANCTIONS REGULATIONS General Definitions § 542.303 Entity. The term entity means a partnership, association, trust, joint venture, corporation, group, subgroup, or...

31 CFR 592.303 - Entity.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false Entity. 592.303 Section 592.303 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS... Entity. The term entity means a partnership, association, trust, joint venture, corporation, or other...

31 CFR 588.303 - Entity.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 31 Money and Finance:Treasury 3 2011-07-01 2011-07-01 false Entity. 588.303 Section 588.303 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS....303 Entity. The term entity means a partnership, association, trust, joint venture, corporation, group...

31 CFR 561.316 - Entity.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 31 Money and Finance:Treasury 3 2011-07-01 2011-07-01 false Entity. 561.316 Section 561.316 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS... Entity. The term entity means a partnership, association, trust, joint venture, corporation, or other...

31 CFR 585.310 - Entity.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false Entity. 585.310 Section 585.310 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS... Definitions § 585.310 Entity. The term entity includes a corporation, partnership, association, or other...

31 CFR 588.303 - Entity.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false Entity. 588.303 Section 588.303 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS....303 Entity. The term entity means a partnership, association, trust, joint venture, corporation, group...

31 CFR 587.303 - Entity.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false Entity. 587.303 Section 587.303 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS... SANCTIONS REGULATIONS General Definitions § 587.303 Entity. The term entity means a partnership, association...

31 CFR 542.303 - Entity.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 31 Money and Finance:Treasury 3 2011-07-01 2011-07-01 false Entity. 542.303 Section 542.303 Money... CONTROL, DEPARTMENT OF THE TREASURY SYRIAN SANCTIONS REGULATIONS General Definitions § 542.303 Entity. The term entity means a partnership, association, trust, joint venture, corporation, group, subgroup, or...

31 CFR 562.303 - Entity.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 31 Money and Finance:Treasury 3 2011-07-01 2011-07-01 false Entity. 562.303 Section 562.303 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS... § 562.303 Entity. The term entity means a partnership, association, trust, joint venture, corporation...

31 CFR 593.303 - Entity.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false Entity. 593.303 Section 593.303 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS... Definitions § 593.303 Entity. The term entity means a partnership, association, trust, joint venture...

31 CFR 592.303 - Entity.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 31 Money and Finance:Treasury 3 2011-07-01 2011-07-01 false Entity. 592.303 Section 592.303 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS... Entity. The term entity means a partnership, association, trust, joint venture, corporation, or other...

31 CFR 586.303 - Entity.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false Entity. 586.303 Section 586.303 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS... REGULATIONS General Definitions § 586.303 Entity. The term entity means a partnership, association, trust...

31 CFR 546.304 - Entity.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 31 Money and Finance:Treasury 3 2011-07-01 2011-07-01 false Entity. 546.304 Section 546.304 Money... CONTROL, DEPARTMENT OF THE TREASURY DARFUR SANCTIONS REGULATIONS General Definitions § 546.304 Entity. The term entity means a partnership, association, trust, joint venture, corporation, group, subgroup, or...

31 CFR 597.306 - Entity.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 31 Money and Finance:Treasury 3 2011-07-01 2011-07-01 false Entity. 597.306 Section 597.306 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS... Definitions § 597.306 Entity. The term entity includes a partnership, association, corporation, or other...

31 CFR 539.303 - Entity.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 31 Money and Finance:Treasury 3 2011-07-01 2011-07-01 false Entity. 539.303 Section 539.303 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS... Definitions § 539.303 Entity. The term entity means a partnership, association, trust, joint venture...

31 CFR 544.303 - Entity.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 31 Money and Finance:Treasury 3 2011-07-01 2011-07-01 false Entity. 544.303 Section 544.303 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS... General Definitions § 544.303 Entity. The term entity means a partnership, association, trust, joint...

31 CFR 570.303 - Entity.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 31 Money and Finance:Treasury 3 2011-07-01 2011-07-01 false Entity. 570.303 Section 570.303 Money... CONTROL, DEPARTMENT OF THE TREASURY LIBYAN SANCTIONS REGULATIONS General Definitions § 570.303 Entity. The term entity means a partnership, association, trust, joint venture, corporation, group, subgroup, or...

31 CFR 540.303 - Entity.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 31 Money and Finance:Treasury 3 2011-07-01 2011-07-01 false Entity. 540.303 Section 540.303 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS... General Definitions § 540.303 Entity. The term entity means a partnership, association, trust, joint...

31 CFR 597.306 - Entity.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false Entity. 597.306 Section 597.306 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS... Definitions § 597.306 Entity. The term entity includes a partnership, association, corporation, or other...

31 CFR 540.303 - Entity.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false Entity. 540.303 Section 540.303 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS... General Definitions § 540.303 Entity. The term entity means a partnership, association, trust, joint...

31 CFR 539.303 - Entity.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false Entity. 539.303 Section 539.303 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS... Definitions § 539.303 Entity. The term entity means a partnership, association, trust, joint venture...

31 CFR 593.303 - Entity.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 31 Money and Finance:Treasury 3 2011-07-01 2011-07-01 false Entity. 593.303 Section 593.303 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS... Definitions § 593.303 Entity. The term entity means a partnership, association, trust, joint venture...

31 CFR 576.304 - Entity.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 31 Money and Finance:Treasury 3 2011-07-01 2011-07-01 false Entity. 576.304 Section 576.304 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS... Definitions § 576.304 Entity. The term entity means a partnership, association, trust, joint venture...

31 CFR 598.303 - Entity.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 31 Money and Finance:Treasury 3 2011-07-01 2011-07-01 false Entity. 598.303 Section 598.303 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS... § 598.303 Entity. The term entity means a partnership, joint venture, association, corporation...

43 CFR 426.10 - Public entities.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 43 Public Lands: Interior 1 2011-10-01 2011-10-01 false Public entities. 426.10 Section 426.10... INTERIOR ACREAGE LIMITATION RULES AND REGULATIONS § 426.10 Public entities. (a) Application of the acreage limitation provisions to public entities. Reclamation does not subject public entities to the acreage...

43 CFR 426.10 - Public entities.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 43 Public Lands: Interior 1 2010-10-01 2010-10-01 false Public entities. 426.10 Section 426.10... INTERIOR ACREAGE LIMITATION RULES AND REGULATIONS § 426.10 Public entities. (a) Application of the acreage limitation provisions to public entities. Reclamation does not subject public entities to the acreage...

47 CFR 101.1429 - Designated entities.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 47 Telecommunication 5 2011-10-01 2011-10-01 false Designated entities. 101.1429 Section 101.1429... Designated entities. (a) Eligibility for small business provisions. (1) A very small business is an entity... exceeding $3 million for the preceding three years. (2) A small business is an entity that, together with...

31 CFR 560.305 - Person; entity.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false Person; entity. 560.305 Section 560.305 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF... § 560.305 Person; entity. (a) The term person means an individual or entity. (b) The term entity means a...

18 CFR 46.5 - Covered entities.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 18 Conservation of Power and Water Resources 1 2011-04-01 2011-04-01 false Covered entities. 46.5... FOR PERSONS HOLDING INTERLOCKING POSITIONS § 46.5 Covered entities. Entities to which the general rule..., or a savings and loan association; (b) Any entity which is authorized by law to underwrite or...

46 CFR 67.41 - Governmental entity.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 46 Shipping 2 2010-10-01 2010-10-01 false Governmental entity. 67.41 Section 67.41 Shipping COAST... DOCUMENTATION OF VESSELS Citizenship Requirements for Vessel Documentation § 67.41 Governmental entity. A governmental entity is a citizen for the purpose of obtaining a vessel document if it is an entity of the...

46 CFR 67.41 - Governmental entity.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 46 Shipping 2 2011-10-01 2011-10-01 false Governmental entity. 67.41 Section 67.41 Shipping COAST... DOCUMENTATION OF VESSELS Citizenship Requirements for Vessel Documentation § 67.41 Governmental entity. A governmental entity is a citizen for the purpose of obtaining a vessel document if it is an entity of the...

18 CFR 46.5 - Covered entities.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 18 Conservation of Power and Water Resources 1 2010-04-01 2010-04-01 false Covered entities. 46.5... FOR PERSONS HOLDING INTERLOCKING POSITIONS § 46.5 Covered entities. Entities to which the general rule..., or a savings and loan association; (b) Any entity which is authorized by law to underwrite or...

42 CFR 6.3 - Eligible entities.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 42 Public Health 1 2010-10-01 2010-10-01 false Eligible entities. 6.3 Section 6.3 Public Health... COVERAGE OF CERTAIN GRANTEES AND INDIVIDUALS § 6.3 Eligible entities. (a) Grantees. Entities eligible for coverage under this part are public and nonprofit private entities receiving Federal funds under any of the...

31 CFR 560.305 - Person; entity.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 31 Money and Finance:Treasury 3 2011-07-01 2011-07-01 false Person; entity. 560.305 Section 560.305 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF... § 560.305 Person; entity. (a) The term person means an individual or entity. (b) The term entity means a...

42 CFR 6.3 - Eligible entities.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 42 Public Health 1 2011-10-01 2011-10-01 false Eligible entities. 6.3 Section 6.3 Public Health... COVERAGE OF CERTAIN GRANTEES AND INDIVIDUALS § 6.3 Eligible entities. (a) Grantees. Entities eligible for coverage under this part are public and nonprofit private entities receiving Federal funds under any of the...

2 CFR 170.310 - Entity.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 2 Grants and Agreements 1 2013-01-01 2013-01-01 false Entity. 170.310 Section 170.310 Grants and Agreements Office of Management and Budget Guidance for Grants and Agreements OFFICE OF MANAGEMENT AND BUDGET... COMPENSATION INFORMATION Definitions § 170.310 Entity. Entity has the meaning given in 2 CFR part 25. ...

2 CFR 170.310 - Entity.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 2 Grants and Agreements 1 2014-01-01 2014-01-01 false Entity. 170.310 Section 170.310 Grants and Agreements Office of Management and Budget Guidance for Grants and Agreements OFFICE OF MANAGEMENT AND BUDGET... INFORMATION Definitions § 170.310 Entity. Entity has the meaning given in 2 CFR part 25. ...

2 CFR 170.310 - Entity.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 2 Grants and Agreements 1 2012-01-01 2012-01-01 false Entity. 170.310 Section 170.310 Grants and Agreements Office of Management and Budget Guidance for Grants and Agreements OFFICE OF MANAGEMENT AND BUDGET... COMPENSATION INFORMATION Definitions § 170.310 Entity. Entity has the meaning given in 2 CFR part 25. ...

2 CFR 170.310 - Entity.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 2 Grants and Agreements 1 2011-01-01 2011-01-01 false Entity. 170.310 Section 170.310 Grants and Agreements Office of Management and Budget Guidance for Grants and Agreements OFFICE OF MANAGEMENT AND BUDGET... INFORMATION Definitions § 170.310 Entity. Entity has the meaning given in 2 CFR part 25. ...

Chemical-induced disease relation extraction with various linguistic features.

PubMed

Gu, Jinghang; Qian, Longhua; Zhou, Guodong

2016-01-01

Understanding the relations between chemicals and diseases is crucial in various biomedical tasks such as new drug discoveries and new therapy developments. While manually mining these relations from the biomedical literature is costly and time-consuming, such a procedure is often difficult to keep up-to-date. To address these issues, the BioCreative-V community proposed a challenging task of automatic extraction of chemical-induced disease (CID) relations in order to benefit biocuration. This article describes our work on the CID relation extraction task on the BioCreative-V tasks. We built a machine learning based system that utilized simple yet effective linguistic features to extract relations with maximum entropy models. In addition to leveraging various features, the hypernym relations between entity concepts derived from the Medical Subject Headings (MeSH)-controlled vocabulary were also employed during both training and testing stages to obtain more accurate classification models and better extraction performance, respectively. We demoted relation extraction between entities in documents to relation extraction between entity mentions. In our system, pairs of chemical and disease mentions at both intra- and inter-sentence levels were first constructed as relation instances for training and testing, then two classification models at both levels were trained from the training examples and applied to the testing examples. Finally, we merged the classification results from mention level to document level to acquire final relations between chemicals and diseases. Our system achieved promisingF-scores of 60.4% on the development dataset and 58.3% on the test dataset using gold-standard entity annotations, respectively. Database URL:https://github.com/JHnlp/BC5CIDTask. © The Author(s) 2016. Published by Oxford University Press.

31 CFR 800.212 - Foreign entity.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 31 Money and Finance:Treasury 3 2011-07-01 2011-07-01 false Foreign entity. 800.212 Section 800... TAKEOVERS BY FOREIGN PERSONS Definitions § 800.212 Foreign entity. (a) The term foreign entity means any... majority of the equity interest in such entity is ultimately owned by U.S. nationals is not a foreign...

2 CFR 25.320 - Entity.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 2 Grants and Agreements 1 2011-01-01 2011-01-01 false Entity. 25.320 Section 25.320 Grants and Agreements Office of Management and Budget Guidance for Grants and Agreements OFFICE OF MANAGEMENT AND BUDGET... CONTRACTOR REGISTRATION Definitions § 25.320 Entity. Entity, as it is used in this part, has the meaning...

[Nonspecific interstitial pneumonitis: a clinicopathologic entity, histologic pattern or unclassified group of heterogeneous interstitial pneumonitis?].

PubMed

Morais, António; Moura, M Conceição Souto; Cruz, M Rosa; Gomes, Isabel

2004-01-01

Nonspecific interstitial pneumonitis (NSIP) initially described by Katzenstein and Fiorelli in 1994, seems to be a distinct clinicopathologic entity among idiopathic interstitial pneumonitis (IIP). Besides different histologic features from other IIP, NSIP is characterized by a better long-term outcome, associated with a better steroids responsiveness than idiopathic pulmonar fibrosis (IPF), where usually were included. Thus, differentiating NSIP from other IIP, namely IPF is very significant, since it has important therapeutic and prognostic implications. NSIP encloses different pathologies, namely those with inflammatory predominance (cellular subtype) or fibrous predominance (fibrosing subtype). NSIP is reviewed and discussed by the authors, after two clinical cases description.

Challenges in Managing Information Extraction

ERIC Educational Resources Information Center

Shen, Warren H.

2009-01-01

This dissertation studies information extraction (IE), the problem of extracting structured information from unstructured data. Example IE tasks include extracting person names from news articles, product information from e-commerce Web pages, street addresses from emails, and names of emerging music bands from blogs. IE is all increasingly…

47 CFR 80.1252 - Designated entities.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 47 Telecommunication 5 2011-10-01 2011-10-01 false Designated entities. 80.1252 Section 80.1252... MARITIME SERVICES Competitive Bidding Procedures § 80.1252 Designated entities. (a) This section addresses certain issues concerning designated entities in maritime communications services subject to competitive...

47 CFR 80.1252 - Designated entities.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 47 Telecommunication 5 2010-10-01 2010-10-01 false Designated entities. 80.1252 Section 80.1252... MARITIME SERVICES Competitive Bidding Procedures § 80.1252 Designated entities. (a) This section addresses certain issues concerning designated entities in maritime communications services subject to competitive...

31 CFR 543.304 - Entity.

Code of Federal Regulations, 2012 CFR

2012-07-01

... CONTROL, DEPARTMENT OF THE TREASURY CôTE D'IVOIRE SANCTIONS REGULATIONS General Definitions § 543.304 Entity. The term entity means a partnership, association, trust, joint venture, corporation, group...

47 CFR 22.229 - Designated entities.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 47 Telecommunication 2 2011-10-01 2011-10-01 false Designated entities. 22.229 Section 22.229... Licensing Requirements and Procedures Competitive Bidding Procedures § 22.229 Designated entities. (a) Eligibility for small business provisions. (1) A very small business is an entity that, together with its...

47 CFR 24.321 - Designated entities.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 47 Telecommunication 2 2011-10-01 2011-10-01 false Designated entities. 24.321 Section 24.321... SERVICES Competitive Bidding Procedures for Narrowband PCS § 24.321 Designated entities. (a) Eligibility for small business provisions. (1) A small business is an entity that, together with its controlling...

47 CFR 22.882 - Designated entities.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 47 Telecommunication 2 2011-10-01 2011-10-01 false Designated entities. 22.882 Section 22.882...-Ground Radiotelephone Service Commercial Aviation Air-Ground Systems § 22.882 Designated entities. (a... business is an entity that, together with its affiliates, its controlling interests and the affiliates of...

47 CFR 27.1218 - Designated entities.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 47 Telecommunication 2 2011-10-01 2011-10-01 false Designated entities. 27.1218 Section 27.1218... COMMUNICATIONS SERVICES Broadband Radio Service and Educational Broadband Service § 27.1218 Designated entities. (a) Eligibility for small business provisions. (1) A small business is an entity that, together with...

47 CFR 24.321 - Designated entities.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 47 Telecommunication 2 2010-10-01 2010-10-01 false Designated entities. 24.321 Section 24.321... SERVICES Competitive Bidding Procedures for Narrowband PCS § 24.321 Designated entities. (a) Eligibility for small business provisions. (1) A small business is an entity that, together with its controlling...

47 CFR 101.538 - Designated entities.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 47 Telecommunication 5 2011-10-01 2011-10-01 false Designated entities. 101.538 Section 101.538... SERVICES 24 GHz Service and Digital Electronic Message Service § 101.538 Designated entities. (a) Eligibility for small business provisions. (1) A very small business is an entity that, together with its...

47 CFR 27.906 - Designated entities.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 47 Telecommunication 2 2010-10-01 2010-10-01 false Designated entities. 27.906 Section 27.906... COMMUNICATIONS SERVICES 1670-1675 MHz Band § 27.906 Designated entities. (a) Eligibility for small business provisions. (1) A very small business is an entity that, together with its controlling interests and...

46 CFR 403.110 - Accounting entities.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 46 Shipping 8 2011-10-01 2011-10-01 false Accounting entities. 403.110 Section 403.110 Shipping... ACCOUNTING SYSTEM General § 403.110 Accounting entities. Each Association shall be a separate accounting entity. However, the records shall be maintained with sufficient particularity to allocate items to each...

47 CFR 27.702 - Designated entities.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 47 Telecommunication 2 2011-10-01 2011-10-01 false Designated entities. 27.702 Section 27.702... COMMUNICATIONS SERVICES Competitive Bidding Procedures for the 698-746 MHz Band § 27.702 Designated entities. (a) Eligibility for small business provisions. (1) An entrepreneur is an entity that, together with its...

47 CFR 27.906 - Designated entities.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 47 Telecommunication 2 2011-10-01 2011-10-01 false Designated entities. 27.906 Section 27.906... COMMUNICATIONS SERVICES 1670-1675 MHz Band § 27.906 Designated entities. (a) Eligibility for small business provisions. (1) A very small business is an entity that, together with its controlling interests and...

47 CFR 22.223 - Designated entities.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 47 Telecommunication 2 2010-10-01 2010-10-01 false Designated entities. 22.223 Section 22.223... Licensing Requirements and Procedures Competitive Bidding Procedures § 22.223 Designated entities. (a) Scope... sections. (b) A small business is an entity that either: (1) Together with its affiliates and controlling...

47 CFR 22.223 - Designated entities.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 47 Telecommunication 2 2011-10-01 2011-10-01 false Designated entities. 22.223 Section 22.223... Licensing Requirements and Procedures Competitive Bidding Procedures § 22.223 Designated entities. (a) Scope... sections. (b) A small business is an entity that either: (1) Together with its affiliates and controlling...

Applying Suffix Rules to Organization Name Recognition

NASA Astrophysics Data System (ADS)

Inui, Takashi; Murakami, Koji; Hashimoto, Taiichi; Utsumi, Kazuo; Ishikawa, Masamichi

This paper presents a method for boosting the performance of the organization name recognition, which is a part of named entity recognition (NER). Although gazetteers (lists of the NEs) have been known as one of the effective features for supervised machine learning approaches on the NER task, the previous methods which have applied the gazetteers to the NER were very simple. The gazetteers have been used just for searching the exact matches between input text and NEs included in them. The proposed method generates regular expression rules from gazetteers, and, with these rules, it can realize a high-coverage searches based on looser matches between input text and NEs. To generate these rules, we focus on the two well-known characteristics of NE expressions; 1) most of NE expressions can be divided into two parts, class-reference part and instance-reference part, 2) for most of NE expressions the class-reference parts are located at the suffix position of them. A pattern mining algorithm runs on the set of NEs in the gazetteers, and some frequent word sequences from which NEs are constructed are found. Then, we employ only word sequences which have the class-reference part at the suffix position as suffix rules. Experimental results showed that our proposed method improved the performance of the organization name recognition, and achieved the 84.58 F-value for evaluation data.

47 CFR 27.502 - Designated entities.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 47 Telecommunication 2 2010-10-01 2010-10-01 false Designated entities. 27.502 Section 27.502... COMMUNICATIONS SERVICES Competitive Bidding Procedures for the 698-806 MHz Band § 27.502 Designated entities. Eligibility for small business provisions: (a)(1) A small business is an entity that, together with its...

47 CFR 27.502 - Designated entities.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 47 Telecommunication 2 2011-10-01 2011-10-01 false Designated entities. 27.502 Section 27.502... COMMUNICATIONS SERVICES Competitive Bidding Procedures for the 698-806 MHz Band § 27.502 Designated entities. Eligibility for small business provisions: (a)(1) A small business is an entity that, together with its...

Evaluation Methods of The Text Entities

ERIC Educational Resources Information Center

Popa, Marius

2006-01-01

The paper highlights some evaluation methods to assess the quality characteristics of the text entities. The main concepts used in building and evaluation processes of the text entities are presented. Also, some aggregated metrics for orthogonality measurements are presented. The evaluation process for automatic evaluation of the text entities is…

47 CFR 101.1429 - Designated entities.

Code of Federal Regulations, 2012 CFR

2012-10-01

... Designated entities. (a) Eligibility for small business provisions. (1) A very small business is an entity... exceeding $3 million for the preceding three years. (2) A small business is an entity that, together with... three years. (b) Bidding credits. A winning bidder that qualifies as a very small business, as defined...

47 CFR 101.1429 - Designated entities.

Code of Federal Regulations, 2013 CFR

2013-10-01

... Designated entities. (a) Eligibility for small business provisions. (1) A very small business is an entity... exceeding $3 million for the preceding three years. (2) A small business is an entity that, together with... three years. (b) Bidding credits. A winning bidder that qualifies as a very small business, as defined...

47 CFR 101.1429 - Designated entities.

Code of Federal Regulations, 2014 CFR

2014-10-01

... Designated entities. (a) Eligibility for small business provisions. (1) A very small business is an entity... exceeding $3 million for the preceding three years. (2) A small business is an entity that, together with... three years. (b) Bidding credits. A winning bidder that qualifies as a very small business, as defined...

42 CFR 438.808 - Exclusion of entities.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 42 Public Health 4 2010-10-01 2010-10-01 false Exclusion of entities. 438.808 Section 438.808... Exclusion of entities. (a) General rule. FFP is available in payments under MCO contracts only if the State excludes from the contracts any entities described in paragraph (b) of this section. (b) Entities that must...

42 CFR 438.808 - Exclusion of entities.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 42 Public Health 4 2011-10-01 2011-10-01 false Exclusion of entities. 438.808 Section 438.808... Exclusion of entities. (a) General rule. FFP is available in payments under MCO contracts only if the State excludes from the contracts any entities described in paragraph (b) of this section. (b) Entities that must...

16 CFR 801.50 - Formation of unincorporated entities.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 16 Commercial Practices 1 2010-01-01 2010-01-01 false Formation of unincorporated entities. 801.50... of unincorporated entities. (a) In the formation of an unincorporated entity (other than in... entity and the unincorporated entity itself may, in the formation transaction, be both acquiring and...

16 CFR 801.50 - Formation of unincorporated entities.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 16 Commercial Practices 1 2011-01-01 2011-01-01 false Formation of unincorporated entities. 801.50... of unincorporated entities. (a) In the formation of an unincorporated entity (other than in... entity and the unincorporated entity itself may, in the formation transaction, be both acquiring and...

Entitymetrics: Measuring the Impact of Entities

PubMed Central

Ding, Ying; Song, Min; Han, Jia; Yu, Qi; Yan, Erjia; Lin, Lili; Chambers, Tamy

2013-01-01

This paper proposes entitymetrics to measure the impact of knowledge units. Entitymetrics highlight the importance of entities embedded in scientific literature for further knowledge discovery. In this paper, we use Metformin, a drug for diabetes, as an example to form an entity-entity citation network based on literature related to Metformin. We then calculate the network features and compare the centrality ranks of biological entities with results from Comparative Toxicogenomics Database (CTD). The comparison demonstrates the usefulness of entitymetrics to detect most of the outstanding interactions manually curated in CTD. PMID:24009660

47 CFR 27.807 - Designated entities.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 47 Telecommunication 2 2011-10-01 2011-10-01 false Designated entities. 27.807 Section 27.807... COMMUNICATIONS SERVICES 1.4 GHz Band § 27.807 Designated entities. (a) Eligibility for small business provisions...-1392 MHz band. (1) A very small business is an entity that, together with its controlling interests and...

47 CFR 27.807 - Designated entities.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 47 Telecommunication 2 2010-10-01 2010-10-01 false Designated entities. 27.807 Section 27.807... COMMUNICATIONS SERVICES 1.4 GHz Band § 27.807 Designated entities. (a) Eligibility for small business provisions...-1392 MHz band. (1) A very small business is an entity that, together with its controlling interests and...

Beneficial Effects of Different Flavonoids on Vascular and Renal Function in L-NAME Hypertensive Rats.

PubMed

Paredes, M Dolores; Romecín, Paola; Atucha, Noemí M; O'Valle, Francisco; Castillo, Julián; Ortiz, M Clara; García-Estañ, Joaquín

2018-04-13

we have evaluated the antihypertensive effect of several flavonoid extracts in a rat model of arterial hypertension caused by chronic administration (6 weeks) of the nitric oxide synthesis inhibitor, L-NAME. Sprague Dawley rats received L-NAME alone or L-NAME plus flavonoid-rich vegetal extracts (Lemon, Grapefruit + Bitter Orange, and Cocoa) or purified flavonoids (Apigenin and Diosmin) for 6 weeks. L-NAME treatment resulted in a marked elevation of blood pressure, and treatment with Apigenin, Lemon Extract, and Grapefruit + Bitter Orange extracts significantly reduced the elevated blood pressure of these animals. Apigenin and some of these flavonoids also ameliorated nitric oxide-dependent and -independent aortic vasodilation and elevated nitrite urinary excretion. End-organ abnormalities such as cardiac infarcts, hyaline arteriopathy and fibrinoid necrosis in coronary arteries and aorta were improved by these treatments, reducing the end-organ vascular damage. the flavonoids included in this study, specially apigenin, may be used as functional food ingredients with potential therapeutic benefit in arterial hypertension.

42 CFR 425.104 - Legal entity.

Code of Federal Regulations, 2012 CFR

2012-10-01

... 42 Public Health 3 2012-10-01 2012-10-01 false Legal entity. 425.104 Section 425.104 Public Health....104 Legal entity. (a) An ACO must be a legal entity, formed under applicable State, Federal, or Tribal... in this part. (b) An ACO formed by two or more otherwise independent ACO participants must be a legal...

42 CFR 425.104 - Legal entity.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 42 Public Health 3 2014-10-01 2014-10-01 false Legal entity. 425.104 Section 425.104 Public Health....104 Legal entity. (a) An ACO must be a legal entity, formed under applicable State, Federal, or Tribal... in this part. (b) An ACO formed by two or more otherwise independent ACO participants must be a legal...

42 CFR 425.104 - Legal entity.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 42 Public Health 3 2013-10-01 2013-10-01 false Legal entity. 425.104 Section 425.104 Public Health....104 Legal entity. (a) An ACO must be a legal entity, formed under applicable State, Federal, or Tribal... in this part. (b) An ACO formed by two or more otherwise independent ACO participants must be a legal...

CNN-based ranking for biomedical entity normalization.

PubMed

Li, Haodi; Chen, Qingcai; Tang, Buzhou; Wang, Xiaolong; Xu, Hua; Wang, Baohua; Huang, Dong

2017-10-03

Most state-of-the-art biomedical entity normalization systems, such as rule-based systems, merely rely on morphological information of entity mentions, but rarely consider their semantic information. In this paper, we introduce a novel convolutional neural network (CNN) architecture that regards biomedical entity normalization as a ranking problem and benefits from semantic information of biomedical entities. The CNN-based ranking method first generates candidates using handcrafted rules, and then ranks the candidates according to their semantic information modeled by CNN as well as their morphological information. Experiments on two benchmark datasets for biomedical entity normalization show that our proposed CNN-based ranking method outperforms traditional rule-based method with state-of-the-art performance. We propose a CNN architecture that regards biomedical entity normalization as a ranking problem. Comparison results show that semantic information is beneficial to biomedical entity normalization and can be well combined with morphological information in our CNN architecture for further improvement.

Algorithms and semantic infrastructure for mutation impact extraction and grounding.

PubMed

Laurila, Jonas B; Naderi, Nona; Witte, René; Riazanov, Alexandre; Kouznetsov, Alexandre; Baker, Christopher J O

2010-12-02

Mutation impact extraction is a hitherto unaccomplished task in state of the art mutation extraction systems. Protein mutations and their impacts on protein properties are hidden in scientific literature, making them poorly accessible for protein engineers and inaccessible for phenotype-prediction systems that currently depend on manually curated genomic variation databases. We present the first rule-based approach for the extraction of mutation impacts on protein properties, categorizing their directionality as positive, negative or neutral. Furthermore protein and mutation mentions are grounded to their respective UniProtKB IDs and selected protein properties, namely protein functions to concepts found in the Gene Ontology. The extracted entities are populated to an OWL-DL Mutation Impact ontology facilitating complex querying for mutation impacts using SPARQL. We illustrate retrieval of proteins and mutant sequences for a given direction of impact on specific protein properties. Moreover we provide programmatic access to the data through semantic web services using the SADI (Semantic Automated Discovery and Integration) framework. We address the problem of access to legacy mutation data in unstructured form through the creation of novel mutation impact extraction methods which are evaluated on a corpus of full-text articles on haloalkane dehalogenases, tagged by domain experts. Our approaches show state of the art levels of precision and recall for Mutation Grounding and respectable level of precision but lower recall for the task of Mutant-Impact relation extraction. The system is deployed using text mining and semantic web technologies with the goal of publishing to a broad spectrum of consumers.

76 FR 78146 - Addition of Certain Persons to the Entity List; and Implementation of Entity List Annual Review...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-12-16

... of the annual review, and revises the entry concerning one person located in Malaysia to add an... Entity List. This rule implements the results of the annual review for entities located in Malaysia... during the annual review, this rule amends one entry currently on the Entity List under Malaysia by...

77 FR 31843 - Unnamed Entity

Federal Register 2010, 2011, 2012, 2013, 2014

2012-05-30

... DEPARTMENT OF ENERGY Federal Energy Regulatory Commission [Docket No. EL12-70-000] Unnamed Entity v. California Independent System Operator Corp.; Notice of Complaint Take notice that on May 21... Commission's Rules of Practice and Procedure, 18 CFR part 206, Unnamed Entity (Complainant) filed a formal...

45 CFR 160.310 - Responsibilities of covered entities.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 45 Public Welfare 1 2010-10-01 2010-10-01 false Responsibilities of covered entities. 160.310... Responsibilities of covered entities. (a) Provide records and compliance reports. A covered entity must keep such... entity has complied or is complying with the applicable administrative simplification provisions. (b...

22 CFR 96.103 - Oversight by accrediting entities.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 22 Foreign Relations 1 2011-04-01 2011-04-01 false Oversight by accrediting entities. 96.103... Relating to Temporary Accreditation § 96.103 Oversight by accrediting entities. (a) The accrediting entity... agency's application for full accreditation when it is filed. The accrediting entity must also...

22 CFR 96.103 - Oversight by accrediting entities.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 22 Foreign Relations 1 2010-04-01 2010-04-01 false Oversight by accrediting entities. 96.103... Relating to Temporary Accreditation § 96.103 Oversight by accrediting entities. (a) The accrediting entity... agency's application for full accreditation when it is filed. The accrediting entity must also...

45 CFR 160.310 - Responsibilities of covered entities.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 45 Public Welfare 1 2011-10-01 2011-10-01 false Responsibilities of covered entities. 160.310... Responsibilities of covered entities. (a) Provide records and compliance reports. A covered entity must keep such... entity has complied or is complying with the applicable administrative simplification provisions. (b...

Multi-Filter String Matching and Human-Centric Entity Matching for Information Extraction

ERIC Educational Resources Information Center

Sun, Chong

2012-01-01

More and more information is being generated in text documents, such as Web pages, emails and blogs. To effectively manage this unstructured information, one broadly used approach includes locating relevant content in documents, extracting structured information and integrating the extracted information for querying, mining or further analysis. In…

Beneficial Effects of Different Flavonoids on Vascular and Renal Function in L-NAME Hypertensive Rats

PubMed Central

Paredes, M. Dolores; Romecín, Paola; Castillo, Julián; Ortiz, M. Clara

2018-01-01

Background: we have evaluated the antihypertensive effect of several flavonoid extracts in a rat model of arterial hypertension caused by chronic administration (6 weeks) of the nitric oxide synthesis inhibitor, L-NAME. Methods: Sprague Dawley rats received L-NAME alone or L-NAME plus flavonoid-rich vegetal extracts (Lemon, Grapefruit + Bitter Orange, and Cocoa) or purified flavonoids (Apigenin and Diosmin) for 6 weeks. Results: L-NAME treatment resulted in a marked elevation of blood pressure, and treatment with Apigenin, Lemon Extract, and Grapefruit + Bitter Orange extracts significantly reduced the elevated blood pressure of these animals. Apigenin and some of these flavonoids also ameliorated nitric oxide-dependent and -independent aortic vasodilation and elevated nitrite urinary excretion. End-organ abnormalities such as cardiac infarcts, hyaline arteriopathy and fibrinoid necrosis in coronary arteries and aorta were improved by these treatments, reducing the end-organ vascular damage. Conclusions: the flavonoids included in this study, specially apigenin, may be used as functional food ingredients with potential therapeutic benefit in arterial hypertension. PMID:29652818

31 CFR 546.304 - Entity.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false Entity. 546.304 Section 546.304 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS CONTROL, DEPARTMENT OF THE TREASURY DARFUR SANCTIONS REGULATIONS General Definitions § 546.304 Entity. The...

31 CFR 510.303 - Entity.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 31 Money and Finance:Treasury 3 2013-07-01 2013-07-01 false Entity. 510.303 Section 510.303 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS CONTROL, DEPARTMENT OF THE TREASURY NORTH KOREA SANCTIONS REGULATIONS General Definitions § 510.303 Entity...

31 CFR 510.303 - Entity.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 31 Money and Finance:Treasury 3 2011-07-01 2011-07-01 false Entity. 510.303 Section 510.303 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS CONTROL, DEPARTMENT OF THE TREASURY NORTH KOREA SANCTIONS REGULATIONS General Definitions § 510.303 Entity...

31 CFR 510.303 - Entity.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 31 Money and Finance:Treasury 3 2014-07-01 2014-07-01 false Entity. 510.303 Section 510.303 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS CONTROL, DEPARTMENT OF THE TREASURY NORTH KOREA SANCTIONS REGULATIONS General Definitions § 510.303 Entity...

31 CFR 510.303 - Entity.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 31 Money and Finance:Treasury 3 2012-07-01 2012-07-01 false Entity. 510.303 Section 510.303 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN ASSETS CONTROL, DEPARTMENT OF THE TREASURY NORTH KOREA SANCTIONS REGULATIONS General Definitions § 510.303 Entity...

Author name recognition in degraded journal images

NASA Astrophysics Data System (ADS)

de Bodard de la Jacopière, Aliette; Likforman-Sulem, Laurence

2006-01-01

A method for extracting names in degraded documents is presented in this article. The documents targeted are images of photocopied scientific journals from various scientific domains. Due to the degradation, there is poor OCR recognition, and pieces of other articles appear on the sides of the image. The proposed approach relies on the combination of a low-level textual analysis and an image-based analysis. The textual analysis extracts robust typographic features, while the image analysis selects image regions of interest through anchor components. We report results on the University of Washington benchmark database.

Chemical-induced disease relation extraction via convolutional neural network.

PubMed

Gu, Jinghang; Sun, Fuqing; Qian, Longhua; Zhou, Guodong

2017-01-01

This article describes our work on the BioCreative-V chemical-disease relation (CDR) extraction task, which employed a maximum entropy (ME) model and a convolutional neural network model for relation extraction at inter- and intra-sentence level, respectively. In our work, relation extraction between entity concepts in documents was simplified to relation extraction between entity mentions. We first constructed pairs of chemical and disease mentions as relation instances for training and testing stages, then we trained and applied the ME model and the convolutional neural network model for inter- and intra-sentence level, respectively. Finally, we merged the classification results from mention level to document level to acquire the final relations between chemical and disease concepts. The evaluation on the BioCreative-V CDR corpus shows the effectiveness of our proposed approach. http://www.biocreative.org/resources/corpora/biocreative-v-cdr-corpus/. © The Author(s) 2017. Published by Oxford University Press.

45 CFR 162.923 - Requirements for covered entities.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 45 Public Welfare 1 2011-10-01 2011-10-01 false Requirements for covered entities. 162.923 Section... Requirements for covered entities. (a) General rule. Except as otherwise provided in this part, if a covered entity conducts, with another covered entity that is required to comply with a transaction standard...

45 CFR 150.307 - Notice to responsible entities.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 45 Public Welfare 1 2011-10-01 2011-10-01 false Notice to responsible entities. 150.307 Section... and Non-Federal Governmental Plans-Civil Money Penalties § 150.307 Notice to responsible entities. If... responsible entity or entities identified under § 150.305. The notice does the following: (a) Describes the...

45 CFR 162.923 - Requirements for covered entities.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 45 Public Welfare 1 2010-10-01 2010-10-01 false Requirements for covered entities. 162.923 Section... Requirements for covered entities. (a) General rule. Except as otherwise provided in this part, if a covered entity conducts, with another covered entity that is required to comply with a transaction standard...

45 CFR 150.307 - Notice to responsible entities.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 45 Public Welfare 1 2010-10-01 2010-10-01 false Notice to responsible entities. 150.307 Section... and Non-Federal Governmental Plans-Civil Money Penalties § 150.307 Notice to responsible entities. If... responsible entity or entities identified under § 150.305. The notice does the following: (a) Describes the...

12 CFR 1237.10 - Limited-life regulated entities.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 12 Banks and Banking 10 2014-01-01 2014-01-01 false Limited-life regulated entities. 1237.10... RECEIVERSHIP Limited-Life Regulated Entities § 1237.10 Limited-life regulated entities. (a) Status. The United... liquidity portfolio of a limited-life regulated entity. (c) Policies and procedures. The Agency may draft...

12 CFR 1237.10 - Limited-life regulated entities.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 12 Banks and Banking 9 2013-01-01 2013-01-01 false Limited-life regulated entities. 1237.10... RECEIVERSHIP Limited-Life Regulated Entities § 1237.10 Limited-life regulated entities. (a) Status. The United... liquidity portfolio of a limited-life regulated entity. (c) Policies and procedures. The Agency may draft...

12 CFR 1237.10 - Limited-life regulated entities.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 12 Banks and Banking 9 2012-01-01 2012-01-01 false Limited-life regulated entities. 1237.10... RECEIVERSHIP Limited-Life Regulated Entities § 1237.10 Limited-life regulated entities. (a) Status. The United... liquidity portfolio of a limited-life regulated entity. (c) Policies and procedures. The Agency may draft...

Survey-based naming conventions for use in OBO Foundry ontology development

PubMed Central

Schober, Daniel; Smith, Barry; Lewis, Suzanna E; Kusnierczyk, Waclaw; Lomax, Jane; Mungall, Chris; Taylor, Chris F; Rocca-Serra, Philippe; Sansone, Susanna-Assunta

2009-01-01

Background A wide variety of ontologies relevant to the biological and medical domains are available through the OBO Foundry portal, and their number is growing rapidly. Integration of these ontologies, while requiring considerable effort, is extremely desirable. However, heterogeneities in format and style pose serious obstacles to such integration. In particular, inconsistencies in naming conventions can impair the readability and navigability of ontology class hierarchies, and hinder their alignment and integration. While other sources of diversity are tremendously complex and challenging, agreeing a set of common naming conventions is an achievable goal, particularly if those conventions are based on lessons drawn from pooled practical experience and surveys of community opinion. Results We summarize a review of existing naming conventions and highlight certain disadvantages with respect to general applicability in the biological domain. We also present the results of a survey carried out to establish which naming conventions are currently employed by OBO Foundry ontologies and to determine what their special requirements regarding the naming of entities might be. Lastly, we propose an initial set of typographic, syntactic and semantic conventions for labelling classes in OBO Foundry ontologies. Conclusion Adherence to common naming conventions is more than just a matter of aesthetics. Such conventions provide guidance to ontology creators, help developers avoid flaws and inaccuracies when editing, and especially when interlinking, ontologies. Common naming conventions will also assist consumers of ontologies to more readily understand what meanings were intended by the authors of ontologies used in annotating bodies of data. PMID:19397794

22 CFR 96.8 - Fees charged by accrediting entities.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 22 Foreign Relations 1 2010-04-01 2010-04-01 false Fees charged by accrediting entities. 96.8... Duties of Accrediting Entities § 96.8 Fees charged by accrediting entities. (a) An accrediting entity may... fees approved by the Secretary. Before approving a schedule of fees proposed by an accrediting entity...

42 CFR 417.484 - Requirement applicable to related entities.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 42 Public Health 3 2011-10-01 2011-10-01 false Requirement applicable to related entities. 417.484... entities. (a) Definition. As used in this section, related entity means any entity that is related to the... agrees to require all related entities to agree that— (1) HHS, the Comptroller General, or their...

42 CFR 417.484 - Requirement applicable to related entities.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 42 Public Health 3 2010-10-01 2010-10-01 false Requirement applicable to related entities. 417.484... entities. (a) Definition. As used in this section, related entity means any entity that is related to the... agrees to require all related entities to agree that— (1) HHS, the Comptroller General, or their...

17 CFR 45.6 - Legal entity identifiers

Code of Federal Regulations, 2014 CFR

2014-04-01

... 17 Commodity and Securities Exchanges 2 2014-04-01 2014-04-01 false Legal entity identifiers 45.6... RECORDKEEPING AND REPORTING REQUIREMENTS § 45.6 Legal entity identifiers Each counterparty to any swap subject... reporting pursuant to this part by means of a single legal entity identifier as specified in this section...

31 CFR 535.301 - Iran; Iranian Entity.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false Iran; Iranian Entity. 535.301 Section 535.301 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF... § 535.301 Iran; Iranian Entity. (a) The term Iran and Iranian Entity includes: (1) The state and the...

31 CFR 535.301 - Iran; Iranian Entity.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 31 Money and Finance:Treasury 3 2011-07-01 2011-07-01 false Iran; Iranian Entity. 535.301 Section 535.301 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF... § 535.301 Iran; Iranian Entity. (a) The term Iran and Iranian Entity includes: (1) The state and the...

31 CFR 535.301 - Iran; Iranian Entity.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 31 Money and Finance:Treasury 3 2013-07-01 2013-07-01 false Iran; Iranian Entity. 535.301 Section... § 535.301 Iran; Iranian Entity. (a) The term Iran and Iranian Entity includes: (1) The state and the Government of Iran as well as any political subdivision, agency, or instrumentality thereof or any territory...

31 CFR 535.301 - Iran; Iranian Entity.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 31 Money and Finance:Treasury 3 2012-07-01 2012-07-01 false Iran; Iranian Entity. 535.301 Section... § 535.301 Iran; Iranian Entity. (a) The term Iran and Iranian Entity includes: (1) The state and the Government of Iran as well as any political subdivision, agency, or instrumentality thereof or any territory...

31 CFR 535.301 - Iran; Iranian Entity.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 31 Money and Finance:Treasury 3 2014-07-01 2014-07-01 false Iran; Iranian Entity. 535.301 Section... § 535.301 Iran; Iranian Entity. (a) The term Iran and Iranian Entity includes: (1) The state and the Government of Iran as well as any political subdivision, agency, or instrumentality thereof or any territory...

14 CFR Sec. 1-6 - Accounting entities.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 14 Aeronautics and Space 4 2011-01-01 2011-01-01 false Accounting entities. Sec. 1-6 Section 1-6... Provisions Sec. 1-6 Accounting entities. (a) Separate accounting records shall be maintained for each air transport entity for which separate reports to the BTS are required to be made by sections 21(g) and for...

BioCreative V CDR task corpus: a resource for chemical disease relation extraction.

PubMed

Li, Jiao; Sun, Yueping; Johnson, Robin J; Sciaky, Daniela; Wei, Chih-Hsuan; Leaman, Robert; Davis, Allan Peter; Mattingly, Carolyn J; Wiegers, Thomas C; Lu, Zhiyong

2016-01-01

Community-run, formal evaluations and manually annotated text corpora are critically important for advancing biomedical text-mining research. Recently in BioCreative V, a new challenge was organized for the tasks of disease named entity recognition (DNER) and chemical-induced disease (CID) relation extraction. Given the nature of both tasks, a test collection is required to contain both disease/chemical annotations and relation annotations in the same set of articles. Despite previous efforts in biomedical corpus construction, none was found to be sufficient for the task. Thus, we developed our own corpus called BC5CDR during the challenge by inviting a team of Medical Subject Headings (MeSH) indexers for disease/chemical entity annotation and Comparative Toxicogenomics Database (CTD) curators for CID relation annotation. To ensure high annotation quality and productivity, detailed annotation guidelines and automatic annotation tools were provided. The resulting BC5CDR corpus consists of 1500 PubMed articles with 4409 annotated chemicals, 5818 diseases and 3116 chemical-disease interactions. Each entity annotation includes both the mention text spans and normalized concept identifiers, using MeSH as the controlled vocabulary. To ensure accuracy, the entities were first captured independently by two annotators followed by a consensus annotation: The average inter-annotator agreement (IAA) scores were 87.49% and 96.05% for the disease and chemicals, respectively, in the test set according to the Jaccard similarity coefficient. Our corpus was successfully used for the BioCreative V challenge tasks and should serve as a valuable resource for the text-mining research community.Database URL: http://www.biocreative.org/tasks/biocreative-v/track-3-cdr/. Published by Oxford University Press 2016. This work is written by US Government employees and is in the public domain in the United States.

47 CFR 27.1218 - Designated entities.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 47 Telecommunication 2 2014-10-01 2014-10-01 false Designated entities. 27.1218 Section 27.1218 Telecommunication FEDERAL COMMUNICATIONS COMMISSION (CONTINUED) COMMON CARRIER SERVICES MISCELLANEOUS WIRELESS COMMUNICATIONS SERVICES Broadband Radio Service and Educational Broadband Service § 27.1218 Designated entities...

47 CFR 27.1218 - Designated entities.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 47 Telecommunication 2 2013-10-01 2013-10-01 false Designated entities. 27.1218 Section 27.1218 Telecommunication FEDERAL COMMUNICATIONS COMMISSION (CONTINUED) COMMON CARRIER SERVICES MISCELLANEOUS WIRELESS COMMUNICATIONS SERVICES Broadband Radio Service and Educational Broadband Service § 27.1218 Designated entities...

78 FR 22270 - Special Fraud Alert: Physician-Owned Entities

Federal Register 2010, 2011, 2012, 2013, 2014

2013-04-15

...] Special Fraud Alert: Physician-Owned Entities AGENCY: Office of Inspector General (OIG), HHS. ACTION... Physician-Owned Entities. Specifically, the Special Fraud Alert addressed physician-owned entities that... publication of the Special Fraud Alert on Physician-Owned Entities, an inadvertent error appeared in the DATES...

Processing new and repeated names: Effects of coreference on repetition priming with speech and fast RSVP

PubMed Central

Camblin, C. Christine; Ledoux, Kerry; Boudewyn, Megan; Gordon, Peter C.; Swaab, Tamara Y.

2006-01-01

Previous research has shown that the process of establishing coreference with a repeated name can affect basic repetition priming. Specifically, repetition priming on some measures can be eliminated for repeated names that corefer with an entity that is prominent in the discourse model. However, the exact nature and timing of this modulating effect of discourse are not yet understood. Here, we present two ERP studies that further probe the nature of repeated name coreference by using naturally produced connected speech and fast-rate RSVP methods of presentation. With speech we found that repetition priming was eliminated for repeated names that coreferred with a prominent antecedent. In contrast, with fast-rate RSVP, we found a main effect of repetition that did not interact with sentence context. This indicates that the creation of a discourse model during comprehension can affect repetition priming, but the nature of this effect may depend on input speed. PMID:16904078

Building entity models through observation and learning

NASA Astrophysics Data System (ADS)

Garcia, Richard; Kania, Robert; Fields, MaryAnne; Barnes, Laura

2011-05-01

To support the missions and tasks of mixed robotic/human teams, future robotic systems will need to adapt to the dynamic behavior of both teammates and opponents. One of the basic elements of this adaptation is the ability to exploit both long and short-term temporal data. This adaptation allows robotic systems to predict/anticipate, as well as influence, future behavior for both opponents and teammates and will afford the system the ability to adjust its own behavior in order to optimize its ability to achieve the mission goals. This work is a preliminary step in the effort to develop online entity behavior models through a combination of learning techniques and observations. As knowledge is extracted from the system through sensor and temporal feedback, agents within the multi-agent system attempt to develop and exploit a basic movement model of an opponent. For the purpose of this work, extraction and exploitation is performed through the use of a discretized two-dimensional game. The game consists of a predetermined number of sentries attempting to keep an unknown intruder agent from penetrating their territory. The sentries utilize temporal data coupled with past opponent observations to hypothesize the probable locations of the opponent and thus optimize their guarding locations.

14 CFR 1-6 - Accounting entities.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 14 Aeronautics and Space 4 2012-01-01 2012-01-01 false Accounting entities. Sec. 1-6 Section Sec. 1-6 Aeronautics and Space OFFICE OF THE SECRETARY, DEPARTMENT OF TRANSPORTATION (AVIATION... General Accounting Provisions Sec. 1-6 Accounting entities. (a) Separate accounting records shall be...

46 CFR 403.110 - Accounting entities.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 46 Shipping 8 2010-10-01 2010-10-01 false Accounting entities. 403.110 Section 403.110 Shipping COAST GUARD (GREAT LAKES PILOTAGE), DEPARTMENT OF HOMELAND SECURITY GREAT LAKES PILOTAGE UNIFORM ACCOUNTING SYSTEM General § 403.110 Accounting entities. Each Association shall be a separate accounting...

Efficient authentication scheme based on near-ring root extraction problem

NASA Astrophysics Data System (ADS)

Muthukumaran, V.; Ezhilmaran, D.

2017-11-01

An authentication protocolis the type of computer communication protocol or cryptography protocol specifically designed for transfer of authentication data between two entities. We have planned a two new entity authentication scheme on the basis of root extraction problem near-ring in this article. We suggest that this problem is suitably difficult to serve as a cryptographic assumption over the platform of near-ring N. The security issues also discussed.

42 CFR 410.145 - Requirements for entities.

Code of Federal Regulations, 2014 CFR

2014-10-01

... PROGRAM SUPPLEMENTARY MEDICAL INSURANCE (SMI) BENEFITS Outpatient Diabetes Self-Management Training and Diabetes Outcome Measurements § 410.145 Requirements for entities. (a) Deemed entities. (1) Except as...

42 CFR 410.145 - Requirements for entities.

Code of Federal Regulations, 2013 CFR

2013-10-01

... PROGRAM SUPPLEMENTARY MEDICAL INSURANCE (SMI) BENEFITS Outpatient Diabetes Self-Management Training and Diabetes Outcome Measurements § 410.145 Requirements for entities. (a) Deemed entities. (1) Except as...

42 CFR 410.145 - Requirements for entities.

Code of Federal Regulations, 2012 CFR

2012-10-01

... PROGRAM SUPPLEMENTARY MEDICAL INSURANCE (SMI) BENEFITS Outpatient Diabetes Self-Management Training and Diabetes Outcome Measurements § 410.145 Requirements for entities. (a) Deemed entities. (1) Except as...

17 CFR 202.8 - Small entity compliance guides.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 17 Commodity and Securities Exchanges 2 2010-04-01 2010-04-01 false Small entity compliance guides. 202.8 Section 202.8 Commodity and Securities Exchanges SECURITIES AND EXCHANGE COMMISSION INFORMAL AND OTHER PROCEDURES § 202.8 Small entity compliance guides. The following small entity compliance guides...

17 CFR 202.8 - Small entity compliance guides.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 17 Commodity and Securities Exchanges 2 2011-04-01 2011-04-01 false Small entity compliance guides. 202.8 Section 202.8 Commodity and Securities Exchanges SECURITIES AND EXCHANGE COMMISSION INFORMAL AND OTHER PROCEDURES § 202.8 Small entity compliance guides. The following small entity compliance guides...

31 CFR 537.312 - Nongovernmental entity in Burma.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false Nongovernmental entity in Burma. 537.312 Section 537.312 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued... Definitions § 537.312 Nongovernmental entity in Burma. The term nongovernmental entity in Burma means a...

31 CFR 537.312 - Nongovernmental entity in Burma.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 31 Money and Finance:Treasury 3 2011-07-01 2011-07-01 false Nongovernmental entity in Burma. 537.312 Section 537.312 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued... Definitions § 537.312 Nongovernmental entity in Burma. The term nongovernmental entity in Burma means a...

26 CFR 301.7701-5 - Domestic and foreign business entities.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 26 Internal Revenue 18 2010-04-01 2010-04-01 false Domestic and foreign business entities. 301... foreign business entities. (a) Domestic and foreign business entities. A business entity (including an entity that is disregarded as separate from its owner under § 301.7701-2(c)) is domestic if it is created...

26 CFR 301.7701-5 - Domestic and foreign business entities.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 26 Internal Revenue 18 2011-04-01 2011-04-01 false Domestic and foreign business entities. 301... foreign business entities. (a) Domestic and foreign business entities. A business entity (including an entity that is disregarded as separate from its owner under § 301.7701-2(c)) is domestic if it is created...

26 CFR 1.892-5 - Controlled commercial entity.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 26 Internal Revenue 9 2010-04-01 2010-04-01 false Controlled commercial entity. 1.892-5 Section 1... (CONTINUED) INCOME TAXES Miscellaneous Provisions § 1.892-5 Controlled commercial entity. (a)-(a)(2...)(B), the term entity means and includes a corporation, a partnership, a trust (including a pension...

26 CFR 1.892-5 - Controlled commercial entity.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 26 Internal Revenue 9 2011-04-01 2011-04-01 false Controlled commercial entity. 1.892-5 Section 1... (CONTINUED) INCOME TAXES (CONTINUED) Miscellaneous Provisions § 1.892-5 Controlled commercial entity. (a)-(a... section 892(a)(2)(B), the term entity means and includes a corporation, a partnership, a trust (including...

22 CFR 96.21 - Choosing an accrediting entity.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 22 Foreign Relations 1 2010-04-01 2010-04-01 false Choosing an accrediting entity. 96.21 Section... Accreditation and Approval § 96.21 Choosing an accrediting entity. (a) An agency that seeks to become accredited must apply to an accrediting entity that is designated to provide accreditation services and that has...

22 CFR 96.21 - Choosing an accrediting entity.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 22 Foreign Relations 1 2011-04-01 2011-04-01 false Choosing an accrediting entity. 96.21 Section... Accreditation and Approval § 96.21 Choosing an accrediting entity. (a) An agency that seeks to become accredited must apply to an accrediting entity that is designated to provide accreditation services and that has...

77 FR 24587 - Addition of Certain Persons to the Entity List; and Implementation of Entity List Annual Review...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-04-25

...; --Mahtab Technical Engineering Company; --Composite Propellant Missile Industry; and --Sanaye Sokhte... entity; 0 (i) By removing the ``Country'' column for South Korea, including the South Korean entity... Technical Engineering Company;. --Composite Propellant Missile Industry; and. --Sanaye Sokhte Morakab (SSM...

Customer and household matching: resolving entity identity in data warehouses

NASA Astrophysics Data System (ADS)

Berndt, Donald J.; Satterfield, Ronald K.

2000-04-01

The data preparation and cleansing tasks necessary to ensure high quality data are among the most difficult challenges faced in data warehousing and data mining projects. The extraction of source data, transformation into new forms, and loading into a data warehouse environment are all time consuming tasks that can be supported by methodologies and tools. This paper focuses on the problem of record linkage or entity matching, tasks that can be very important in providing high quality data. Merging two or more large databases into a single integrated system is a difficult problem in many industries, especially in the wake of acquisitions. For example, managing customer lists can be challenging when duplicate entries, data entry problems, and changing information conspire to make data quality an elusive target. Common tasks with regard to customer lists include customer matching to reduce duplicate entries and household matching to group customers. These often O(n2) problems can consume significant resources, both in computing infrastructure and human oversight, and the goal of high accuracy in the final integrated database can be difficult to assure. This paper distinguishes between attribute corruption and entity corruption, discussing the various impacts on quality. A metajoin operator is proposed and used to organize past and current entity matching techniques. Finally, a logistic regression approach to implementing the metajoin operator is discussed and illustrated with an example. The metajoin can be used to determine whether two records match, don't match, or require further evaluation by human experts. Properly implemented, the metajoin operator could allow the integration of individual databases with greater accuracy and lower cost.

76 FR 63184 - Addition of Certain Persons on the Entity List; Implementation of Entity List Annual Review...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-10-12

... as a result of requests for removal submitted by each of these three persons, a review of information...-country) to entities identified on the Entity List require a license from the Bureau of Industry and... effective October 12, 2011. FOR FURTHER INFORMATION CONTACT: Karen Nies-Vogel, Chair, End-User Review...

45 CFR 158.603 - Notice to responsible entities.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 45 Public Welfare 1 2011-10-01 2011-10-01 false Notice to responsible entities. 158.603 Section... Notice to responsible entities. If HHS learns of a potential violation described in § 158.602 of this... violation. (b) Provide 30 days from the date of the notice for the responsible entity to respond and to...

Spatio-structural granularity of biological material entities

PubMed Central

2010-01-01

Background With the continuously increasing demands on knowledge- and data-management that databases have to meet, ontologies and the theories of granularity they use become more and more important. Unfortunately, currently used theories and schemes of granularity unnecessarily limit the performance of ontologies due to two shortcomings: (i) they do not allow the integration of multiple granularity perspectives into one granularity framework; (ii) they are not applicable to cumulative-constitutively organized material entities, which cover most of the biomedical material entities. Results The above mentioned shortcomings are responsible for the major inconsistencies in currently used spatio-structural granularity schemes. By using the Basic Formal Ontology (BFO) as a top-level ontology and Keet's general theory of granularity, a granularity framework is presented that is applicable to cumulative-constitutively organized material entities. It provides a scheme for granulating complex material entities into their constitutive and regional parts by integrating various compositional and spatial granularity perspectives. Within a scale dependent resolution perspective, it even allows distinguishing different types of representations of the same material entity. Within other scale dependent perspectives, which are based on specific types of measurements (e.g. weight, volume, etc.), the possibility of organizing instances of material entities independent of their parthood relations and only according to increasing measures is provided as well. All granularity perspectives are connected to one another through overcrossing granularity levels, together forming an integrated whole that uses the compositional object perspective as an integrating backbone. This granularity framework allows to consistently assign structural granularity values to all different types of material entities. Conclusions The here presented framework provides a spatio-structural granularity framework

14 CFR 252.19 - Single-entity charters.

Code of Federal Regulations, 2010 CFR

2010-01-01

...) ECONOMIC REGULATIONS SMOKING ABOARD AIRCRAFT § 252.19 Single-entity charters. On single-entity charters... flights is given notice of the smoking procedures for the flight at the time he or she first makes...

14 CFR 252.19 - Single-entity charters.

Code of Federal Regulations, 2011 CFR

2011-01-01

...) ECONOMIC REGULATIONS SMOKING ABOARD AIRCRAFT § 252.19 Single-entity charters. On single-entity charters... flights is given notice of the smoking procedures for the flight at the time he or she first makes...

14 CFR 252.19 - Single-entity charters.

Code of Federal Regulations, 2013 CFR

2013-01-01

...) ECONOMIC REGULATIONS SMOKING ABOARD AIRCRAFT § 252.19 Single-entity charters. On single-entity charters... flights is given notice of the smoking procedures for the flight at the time he or she first makes...

14 CFR 252.19 - Single-entity charters.

Code of Federal Regulations, 2014 CFR

2014-01-01

...) ECONOMIC REGULATIONS SMOKING ABOARD AIRCRAFT § 252.19 Single-entity charters. On single-entity charters... flights is given notice of the smoking procedures for the flight at the time he or she first makes...

14 CFR 252.19 - Single-entity charters.

Code of Federal Regulations, 2012 CFR

2012-01-01

...) ECONOMIC REGULATIONS SMOKING ABOARD AIRCRAFT § 252.19 Single-entity charters. On single-entity charters... flights is given notice of the smoking procedures for the flight at the time he or she first makes...

[Mucosal Schwann cells hamartoma: Review of a recently described entity].

PubMed

García-Molina, Francisco; Ruíz-Macia, José Antonio; Sola, Joaquin

Neural lesions of the colon may be masses (schwannomas and neurofibromas) or, more frequently, small polyps including perineuromas, ganglioneuromas and granular cell tumors. Some neural lesions are associated with congenital syndromes (neurofibromatosis-1, multiple endocrine neoplasia-2B). Recently, a new entity has been described named mucosal Schwann cell hamartoma, consisting of an intramucosal neural proliferation; to date, less than forty cases have been reported. We report a further case in a patient from whom a polyp was extirpated during colonoscopy screening. Histologically, the polyp showed a lamina propia that contained spindle-shaped cells of neural aspect which could only be identified after a histochemical and immunohistochemical study. Copyright © 2017 Sociedad Española de Anatomía Patológica. Publicado por Elsevier España, S.L.U. All rights reserved.

78 FR 3317 - Removal of Persons From the Entity List Based on Removal Request; Implementation of Entity List...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-01-16

... the Entity List. The ERC's decision to remove these two persons took into account their cooperation... INFORMATION CONTACT: Karen Nies-Vogel, Chair, End-User Review Committee, Office of the Assistant Secretary..., Fax: (202) 482-3911, Email: [email protected] . SUPPLEMENTARY INFORMATION: Background The Entity List...

Symbolic emblems of the Levantine Aurignacians as a regional entity identifier (Hayonim Cave, Lower Galilee, Israel).

PubMed

Tejero, José-Miguel; Belfer-Cohen, Anna; Bar-Yosef, Ofer; Gutkin, Vitaly; Rabinovich, Rivka

2018-05-15

The Levantine Aurignacian is a unique phenomenon in the local Upper Paleolithic sequence, showing greater similarity to the West European classic Aurignacian than to the local Levantine archaeological entities preceding and following it. Herewith we highlight another unique characteristic of this entity, namely, the presence of symbolic objects in the form of notched bones (mostly gazelle scapulae) from the Aurignacian levels of Hayonim Cave, Lower Galilee, Israel. Through both macroscopic and microscopic analyses of the items, we suggest that they are not mere cut marks but rather are intentional (decorative?) human-made markings. The significance of this evidence for symbolic behavior is discussed in its chrono-cultural and geographical contexts. Notched bones are among the oldest symbolic expressions of anatomically modern humans. However, unlike other Paleolithic sites where such findings were reported in single numbers, the number of these items recovered at Hayonim Cave is sufficient to assume they possibly served as an emblem of the Levantine Aurignacian.

12 CFR 1238.7 - Publication of results by regulated entities.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 12 Banks and Banking 10 2014-01-01 2014-01-01 false Publication of results by regulated entities... TESTING OF REGULATED ENTITIES § 1238.7 Publication of results by regulated entities. (a) Public disclosure of results required for stress tests of regulated entities. The Enterprises must disclose publicly a...

37 CFR 381.2 - Definition of public broadcasting entity.

Code of Federal Regulations, 2010 CFR

2010-07-01

... broadcasting entity. 381.2 Section 381.2 Patents, Trademarks, and Copyrights COPYRIGHT ROYALTY BOARD, LIBRARY... WITH NONCOMMERCIAL EDUCATIONAL BROADCASTING § 381.2 Definition of public broadcasting entity. As used in this part, the term public broadcasting entity means a noncommercial educational broadcast station...

37 CFR 253.2 - Definition of public broadcasting entity.

Code of Federal Regulations, 2011 CFR

2011-07-01

... broadcasting entity. 253.2 Section 253.2 Patents, Trademarks, and Copyrights COPYRIGHT OFFICE, LIBRARY OF... CONNECTION WITH NONCOMMERCIAL EDUCATIONAL BROADCASTING § 253.2 Definition of public broadcasting entity. As used in this part, the term public broadcasting entity means a noncommercial educational broadcast...

37 CFR 381.2 - Definition of public broadcasting entity.

Code of Federal Regulations, 2011 CFR

2011-07-01

... broadcasting entity. 381.2 Section 381.2 Patents, Trademarks, and Copyrights COPYRIGHT ROYALTY BOARD, LIBRARY... WITH NONCOMMERCIAL EDUCATIONAL BROADCASTING § 381.2 Definition of public broadcasting entity. As used in this part, the term public broadcasting entity means a noncommercial educational broadcast station...

37 CFR 253.2 - Definition of public broadcasting entity.

Code of Federal Regulations, 2010 CFR

2010-07-01

... broadcasting entity. 253.2 Section 253.2 Patents, Trademarks, and Copyrights COPYRIGHT OFFICE, LIBRARY OF... CONNECTION WITH NONCOMMERCIAL EDUCATIONAL BROADCASTING § 253.2 Definition of public broadcasting entity. As used in this part, the term public broadcasting entity means a noncommercial educational broadcast...

12 CFR 1238.6 - Post-assessment actions by regulated entities.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 12 Banks and Banking 10 2014-01-01 2014-01-01 false Post-assessment actions by regulated entities... TESTING OF REGULATED ENTITIES § 1238.6 Post-assessment actions by regulated entities. Each regulated entity shall take the results of the stress test conducted under § 1238.3 into account in making changes...

22 CFR 140.9 - Other non-governmental entities and individuals.

Code of Federal Regulations, 2014 CFR

2014-04-01

.... Section 140.9 applies to private voluntary agencies, educational institutions, for-profit firms, other non-governmental entities and private individuals. A non-governmental entity that is not organized under the laws... suspect that a proposed U.S. non-governmental entity or a key individual of such entity may be or may have...

22 CFR 140.9 - Other non-governmental entities and individuals.

Code of Federal Regulations, 2012 CFR

2012-04-01

.... Section 140.9 applies to private voluntary agencies, educational institutions, for-profit firms, other non-governmental entities and private individuals. A non-governmental entity that is not organized under the laws... suspect that a proposed U.S. non-governmental entity or a key individual of such entity may be or may have...

22 CFR 140.9 - Other non-governmental entities and individuals.

Code of Federal Regulations, 2013 CFR

2013-04-01

.... Section 140.9 applies to private voluntary agencies, educational institutions, for-profit firms, other non-governmental entities and private individuals. A non-governmental entity that is not organized under the laws... suspect that a proposed U.S. non-governmental entity or a key individual of such entity may be or may have...

26 CFR 301.7701-3 - Classification of certain business entities.

Code of Federal Regulations, 2013 CFR

2013-04-01

... the new target corporation under section 338. (iii) Application to successive elections in tiered... of an entity does not result in the creation of a new entity for purposes of the sixty month... entity separate from A when A becomes the only member of X. X, however, is not treated as a new entity...

26 CFR 301.7701-3 - Classification of certain business entities.

Code of Federal Regulations, 2012 CFR

2012-04-01

... the new target corporation under section 338. (iii) Application to successive elections in tiered... of an entity does not result in the creation of a new entity for purposes of the sixty month... entity separate from A when A becomes the only member of X. X, however, is not treated as a new entity...

26 CFR 301.7701-3 - Classification of certain business entities.

Code of Federal Regulations, 2011 CFR

2011-04-01

... the new target corporation under section 338. (iii) Application to successive elections in tiered... of an entity does not result in the creation of a new entity for purposes of the sixty month... entity separate from A when A becomes the only member of X. X, however, is not treated as a new entity...

26 CFR 301.7701-3 - Classification of certain business entities.

Code of Federal Regulations, 2014 CFR

2014-04-01

... the new target corporation under section 338. (iii) Application to successive elections in tiered... of an entity does not result in the creation of a new entity for purposes of the sixty month... entity separate from A when A becomes the only member of X. X, however, is not treated as a new entity...

18 CFR 39.8 - Delegation to a Regional Entity.

Code of Federal Regulations, 2011 CFR

2011-04-01

... Regional Entity. 39.8 Section 39.8 Conservation of Power and Water Resources FEDERAL ENERGY REGULATORY... OF ELECTRIC RELIABILITY STANDARDS § 39.8 Delegation to a Regional Entity. (a) The Electric Reliability Organization may enter into an agreement to delegate authority to a Regional Entity for the...

49 CFR 37.29 - Private entities providing taxi service.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 49 Transportation 1 2010-10-01 2010-10-01 false Private entities providing taxi service. 37.29... INDIVIDUALS WITH DISABILITIES (ADA) Applicability § 37.29 Private entities providing taxi service. (a) Providers of taxi service are subject to the requirements of this part for private entities primarily...

45 CFR 162.510 - Full implementation requirements: Covered entities.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 45 Public Welfare 1 2013-10-01 2013-10-01 false Full implementation requirements: Covered entities. 162.510 Section 162.510 Public Welfare DEPARTMENT OF HEALTH AND HUMAN SERVICES ADMINISTRATIVE DATA... Plans § 162.510 Full implementation requirements: Covered entities. (a) A covered entity must use an...

7 CFR 25.401 - Responsibility of lead managing entity.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 7 Agriculture 1 2013-01-01 2013-01-01 false Responsibility of lead managing entity. 25.401 Section... COMMUNITIES Post-Designation Requirements § 25.401 Responsibility of lead managing entity. (a) Financial. The lead managing entity will be responsible for strategic plan program activities and monitoring the...

7 CFR 25.401 - Responsibility of lead managing entity.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 7 Agriculture 1 2012-01-01 2012-01-01 false Responsibility of lead managing entity. 25.401 Section... COMMUNITIES Post-Designation Requirements § 25.401 Responsibility of lead managing entity. (a) Financial. The lead managing entity will be responsible for strategic plan program activities and monitoring the...

7 CFR 25.401 - Responsibility of lead managing entity.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 7 Agriculture 1 2014-01-01 2014-01-01 false Responsibility of lead managing entity. 25.401 Section... COMMUNITIES Post-Designation Requirements § 25.401 Responsibility of lead managing entity. (a) Financial. The lead managing entity will be responsible for strategic plan program activities and monitoring the...

49 CFR 37.29 - Private entities providing taxi service.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 49 Transportation 1 2011-10-01 2011-10-01 false Private entities providing taxi service. 37.29... INDIVIDUALS WITH DISABILITIES (ADA) Applicability § 37.29 Private entities providing taxi service. (a) Providers of taxi service are subject to the requirements of this part for private entities primarily...

Anatomical entity mention recognition at literature scale

PubMed Central

Pyysalo, Sampo; Ananiadou, Sophia

2014-01-01

Motivation: Anatomical entities ranging from subcellular structures to organ systems are central to biomedical science, and mentions of these entities are essential to understanding the scientific literature. Despite extensive efforts to automatically analyze various aspects of biomedical text, there have been only few studies focusing on anatomical entities, and no dedicated methods for learning to automatically recognize anatomical entity mentions in free-form text have been introduced. Results: We present AnatomyTagger, a machine learning-based system for anatomical entity mention recognition. The system incorporates a broad array of approaches proposed to benefit tagging, including the use of Unified Medical Language System (UMLS)- and Open Biomedical Ontologies (OBO)-based lexical resources, word representations induced from unlabeled text, statistical truecasing and non-local features. We train and evaluate the system on a newly introduced corpus that substantially extends on previously available resources, and apply the resulting tagger to automatically annotate the entire open access scientific domain literature. The resulting analyses have been applied to extend services provided by the Europe PubMed Central literature database. Availability and implementation: All tools and resources introduced in this work are available from http://nactem.ac.uk/anatomytagger. Contact: sophia.ananiadou@manchester.ac.uk Supplementary Information: Supplementary data are available at Bioinformatics online. PMID:24162468

7 CFR 1486.202 - Are there any ineligible entities?

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 10 2010-01-01 2010-01-01 false Are there any ineligible entities? 1486.202 Section... Eligibility, Applications, and Funding § 1486.202 Are there any ineligible entities? Foreign organizations, whether government or private, may participate as third parties in activities carried out by U.S. entities...

45 CFR 162.610 - Implementation specifications for covered entities.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 45 Public Welfare 1 2011-10-01 2011-10-01 false Implementation specifications for covered entities... Implementation specifications for covered entities. (a) The standard unique employer identifier of an employer of... Statement, from the employer. (b) A covered entity must use the standard unique employer identifier (EIN) of...

7 CFR 760.115 - Deceased individuals or dissolved entities.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 7 2010-01-01 2010-01-01 false Deceased individuals or dissolved entities. 760.115... Agricultural Disaster Assistance Programs § 760.115 Deceased individuals or dissolved entities. (a) Payments... or is a dissolved entity if a representative, who currently has authority to enter into a contract...

7 CFR 1486.202 - Are there any ineligible entities?

Code of Federal Regulations, 2011 CFR

2011-01-01

... 7 Agriculture 10 2011-01-01 2011-01-01 false Are there any ineligible entities? 1486.202 Section... Eligibility, Applications, and Funding § 1486.202 Are there any ineligible entities? Foreign organizations, whether government or private, may participate as third parties in activities carried out by U.S. entities...

45 CFR 162.610 - Implementation specifications for covered entities.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 45 Public Welfare 1 2010-10-01 2010-10-01 false Implementation specifications for covered entities... Implementation specifications for covered entities. (a) The standard unique employer identifier of an employer of... Statement, from the employer. (b) A covered entity must use the standard unique employer identifier (EIN) of...

42 CFR 422.592 - Reconsideration by an independent entity.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 42 Public Health 3 2011-10-01 2011-10-01 false Reconsideration by an independent entity. 422.592... and Appeals § 422.592 Reconsideration by an independent entity. (a) When the MA organization affirms... be reviewed and resolved by an independent, outside entity that contracts with CMS. (b) The...

42 CFR 422.592 - Reconsideration by an independent entity.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 42 Public Health 3 2010-10-01 2010-10-01 false Reconsideration by an independent entity. 422.592... and Appeals § 422.592 Reconsideration by an independent entity. (a) When the MA organization affirms... be reviewed and resolved by an independent, outside entity that contracts with CMS. (b) The...

7 CFR 760.115 - Deceased individuals or dissolved entities.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 7 Agriculture 7 2012-01-01 2012-01-01 false Deceased individuals or dissolved entities. 760.115... Agricultural Disaster Assistance Programs § 760.115 Deceased individuals or dissolved entities. (a) Payments... or is a dissolved entity if a representative, who currently has authority to enter into a contract...

7 CFR 760.115 - Deceased individuals or dissolved entities.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 7 Agriculture 7 2013-01-01 2013-01-01 false Deceased individuals or dissolved entities. 760.115... Agricultural Disaster Assistance Programs § 760.115 Deceased individuals or dissolved entities. (a) Payments... or is a dissolved entity if a representative, who currently has authority to enter into a contract...

7 CFR 760.115 - Deceased individuals or dissolved entities.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 7 Agriculture 7 2014-01-01 2014-01-01 false Deceased individuals or dissolved entities. 760.115... Agricultural Disaster Assistance Programs § 760.115 Deceased individuals or dissolved entities. (a) Payments... or is a dissolved entity if a representative, who currently has authority to enter into a contract...

7 CFR 760.115 - Deceased individuals or dissolved entities.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 7 Agriculture 7 2011-01-01 2011-01-01 false Deceased individuals or dissolved entities. 760.115... Agricultural Disaster Assistance Programs § 760.115 Deceased individuals or dissolved entities. (a) Payments... or is a dissolved entity if a representative, who currently has authority to enter into a contract...

42 CFR 410.145 - Requirements for entities.

Code of Federal Regulations, 2010 CFR

2010-10-01

... PROGRAM SUPPLEMENTARY MEDICAL INSURANCE (SMI) BENEFITS Outpatient Diabetes Self-Management Training and... documentation and is fully accredited (and periodically reaccredited) by an organization approved by CMS under § 410.142. (ii) The entity is not accredited by an organization that owns or controls the entity. (2...

42 CFR 410.145 - Requirements for entities.

Code of Federal Regulations, 2011 CFR

2011-10-01

... PROGRAM SUPPLEMENTARY MEDICAL INSURANCE (SMI) BENEFITS Outpatient Diabetes Self-Management Training and... documentation and is fully accredited (and periodically reaccredited) by an organization approved by CMS under § 410.142. (ii) The entity is not accredited by an organization that owns or controls the entity. (2...

Entity- Version 1.0

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

Hart, Brian; Oppel, Fred; Rigdon, Brian

2012-09-13

This package contains classes that capture high-level aspects of characters and vehicles. Vehicles manage seats and riders. Vehicles and characters now can be configured to compose different behaviors and have certain capabilities, by adding them through xml data. These behaviors and capabilities are not included in this package, but instead are part of other packages such as mobility behavior, path planning, sight, sound. Entity is not dependent on these other packages. This package also contains the icons used for Umbra applications Dante Scenario Editor, Dante Tabletop and OpShed. This assertion includes a managed C++ wrapper code (EntityWrapper) to enable C#more » applications, such as Dante Scenario Editor, Dante Tabletop, and OpShed, to incorporate this library.« less

12 CFR 607.4 - Assessment of other System entities.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 12 Banks and Banking 6 2011-01-01 2011-01-01 false Assessment of other System entities. 607.4... APPORTIONMENT OF ADMINISTRATIVE EXPENSES § 607.4 Assessment of other System entities. (a)(1) Unless otherwise... section, other System entities will be assessed for estimated direct expenses plus an allocated portion of...

12 CFR 607.4 - Assessment of other System entities.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 12 Banks and Banking 6 2010-01-01 2010-01-01 false Assessment of other System entities. 607.4... APPORTIONMENT OF ADMINISTRATIVE EXPENSES § 607.4 Assessment of other System entities. (a)(1) Unless otherwise... section, other System entities will be assessed for estimated direct expenses plus an allocated portion of...

7 CFR 760.908 - Deceased individuals or dissolved entities.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 7 2010-01-01 2010-01-01 false Deceased individuals or dissolved entities. 760.908... § 760.908 Deceased individuals or dissolved entities. (a) Payments may be made for eligible losses suffered by an eligible participant who is now a deceased individual or is a dissolved entity if a...

7 CFR 1413.113 - Deceased individuals or dissolved entities.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 7 Agriculture 10 2011-01-01 2011-01-01 false Deceased individuals or dissolved entities. 1413.113... PROGRAMS Durum Wheat Quality Program § 1413.113 Deceased individuals or dissolved entities. (a) Payment may... individual or is a dissolved entity if a representative who currently has authority to enter into a contract...

7 CFR 760.908 - Deceased individuals or dissolved entities.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 7 Agriculture 7 2013-01-01 2013-01-01 false Deceased individuals or dissolved entities. 760.908... § 760.908 Deceased individuals or dissolved entities. (a) Payments may be made for eligible losses suffered by an eligible participant who is now a deceased individual or is a dissolved entity if a...

7 CFR 1413.113 - Deceased individuals or dissolved entities.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 7 Agriculture 10 2012-01-01 2012-01-01 false Deceased individuals or dissolved entities. 1413.113... PROGRAMS Durum Wheat Quality Program § 1413.113 Deceased individuals or dissolved entities. (a) Payment may... individual or is a dissolved entity if a representative who currently has authority to enter into a contract...

7 CFR 760.908 - Deceased individuals or dissolved entities.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 7 Agriculture 7 2012-01-01 2012-01-01 false Deceased individuals or dissolved entities. 760.908... § 760.908 Deceased individuals or dissolved entities. (a) Payments may be made for eligible losses suffered by an eligible participant who is now a deceased individual or is a dissolved entity if a...

7 CFR 1413.113 - Deceased individuals or dissolved entities.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 7 Agriculture 10 2013-01-01 2013-01-01 false Deceased individuals or dissolved entities. 1413.113... PROGRAMS Durum Wheat Quality Program § 1413.113 Deceased individuals or dissolved entities. (a) Payment may... individual or is a dissolved entity if a representative who currently has authority to enter into a contract...

7 CFR 760.908 - Deceased individuals or dissolved entities.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 7 Agriculture 7 2014-01-01 2014-01-01 false Deceased individuals or dissolved entities. 760.908... § 760.908 Deceased individuals or dissolved entities. (a) Payments may be made for eligible losses suffered by an eligible participant who is now a deceased individual or is a dissolved entity if a...

7 CFR 1413.113 - Deceased individuals or dissolved entities.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 7 Agriculture 10 2014-01-01 2014-01-01 false Deceased individuals or dissolved entities. 1413.113... PROGRAMS Durum Wheat Quality Program § 1413.113 Deceased individuals or dissolved entities. (a) Payment may... individual or is a dissolved entity if a representative who currently has authority to enter into a contract...

7 CFR 760.908 - Deceased individuals or dissolved entities.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 7 Agriculture 7 2011-01-01 2011-01-01 false Deceased individuals or dissolved entities. 760.908... § 760.908 Deceased individuals or dissolved entities. (a) Payments may be made for eligible losses suffered by an eligible participant who is now a deceased individual or is a dissolved entity if a...

Mutant swarms of a totivirus-like entities are present in the red macroalga Chondrus crispus and have been partially transferred to the nuclear genome.

PubMed

Rousvoal, Sylvie; Bouyer, Betty; López-Cristoffanini, Camilo; Boyen, Catherine; Collén, Jonas

2016-08-01

Chondrus crispus Stackhouse (Gigartinales) is a red seaweed found on North Atlantic rocky shores. Electrophoresis of RNA extracts showed a prominent band with a size of around 6,000 bp. Sequencing of the band revealed several sequences with similarity to totiviruses, double-stranded RNA viruses that normally infect fungi. This virus-like entity was named C. crispus virus (CcV). It should probably be regarded as an extreme viral quasispecies or a mutant swarm since low identity (<65%) was found between sequences. Totiviruses typically code for two genes: one capsid gene (gag) and one RNA-dependent RNA polymerase gene (pol) with a pseudoknot structure between the genes. Both the genes and the intergenic structures were found in the CcV sequences. A nonidentical gag gene was also found in the nuclear genome of C. crispus, with associated expressed sequence tags (EST) and upstream regulatory features. The gene was presumably horizontally transferred from the virus to the alga. Similar dsRNA bands were seen in extracts from different life cycle stages of C. crispus and from all geographic locations tested. In addition, similar bands were also observed in RNA extractions from other red algae; however, the significance of this apparently widespread phenomenon is unknown. Neither phenotype caused by the infection nor any virus particles or capsid proteins were identified; thus, the presence of viral particles has not been validated. These findings increase the known host range of totiviruses to include marine red algae. © 2016 Phycological Society of America.

42 CFR 410.144 - Quality standards for deemed entities.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 42 Public Health 2 2013-10-01 2013-10-01 false Quality standards for deemed entities. 410.144...-Management Training and Diabetes Outcome Measurements § 410.144 Quality standards for deemed entities. An organization approved and recognized by CMS may accredit an entity to meet one of the following sets of quality...

42 CFR 410.144 - Quality standards for deemed entities.

Code of Federal Regulations, 2012 CFR

2012-10-01

... 42 Public Health 2 2012-10-01 2012-10-01 false Quality standards for deemed entities. 410.144...-Management Training and Diabetes Outcome Measurements § 410.144 Quality standards for deemed entities. An organization approved and recognized by CMS may accredit an entity to meet one of the following sets of quality...

42 CFR 410.144 - Quality standards for deemed entities.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 42 Public Health 2 2010-10-01 2010-10-01 false Quality standards for deemed entities. 410.144...-Management Training and Diabetes Outcome Measurements § 410.144 Quality standards for deemed entities. An organization approved and recognized by CMS may accredit an entity to meet one of the following sets of quality...

42 CFR 410.144 - Quality standards for deemed entities.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 42 Public Health 2 2014-10-01 2014-10-01 false Quality standards for deemed entities. 410.144...-Management Training and Diabetes Outcome Measurements § 410.144 Quality standards for deemed entities. An organization approved and recognized by CMS may accredit an entity to meet one of the following sets of quality...

42 CFR 410.144 - Quality standards for deemed entities.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 42 Public Health 2 2011-10-01 2011-10-01 false Quality standards for deemed entities. 410.144...-Management Training and Diabetes Outcome Measurements § 410.144 Quality standards for deemed entities. An organization approved and recognized by CMS may accredit an entity to meet one of the following sets of quality...

42 CFR 6.5 - Deeming process for eligible entities.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 42 Public Health 1 2010-10-01 2010-10-01 false Deeming process for eligible entities. 6.5 Section 6.5 Public Health PUBLIC HEALTH SERVICE, DEPARTMENT OF HEALTH AND HUMAN SERVICES GENERAL PROVISIONS... entities. Eligible entities will be covered by this part only on and after the effective date of a...

42 CFR 6.5 - Deeming process for eligible entities.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 42 Public Health 1 2011-10-01 2011-10-01 false Deeming process for eligible entities. 6.5 Section 6.5 Public Health PUBLIC HEALTH SERVICE, DEPARTMENT OF HEALTH AND HUMAN SERVICES GENERAL PROVISIONS... entities. Eligible entities will be covered by this part only on and after the effective date of a...

7 CFR 1415.18 - Easement transfer to eligible entities.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 7 Agriculture 10 2011-01-01 2011-01-01 false Easement transfer to eligible entities. 1415.18... § 1415.18 Easement transfer to eligible entities. (a) NRCS may transfer title of ownership to an easement to an eligible entity to hold and enforce an easement if: (1) The Chief determines that transfer will...

Cystic renal tumors: new entities and novel concepts.

PubMed

Moch, Holger

2010-05-01

Cystic renal neoplasms and renal epithelial stromal tumors are diagnostically challenging and represent some novel tumor entities. In this article, clinical and pathologic features of established and novel entities are discussed. Predominantly cystic renal tumors include cystic nephroma/mixed epithelial and stromal tumor, synovial sarcoma, and multilocular cystic renal cell carcinoma. These entities are own tumor entities of the 2004 WHO classification of renal tumors. Tubulocystic carcinoma and acquired cystic disease-associated renal cell carcinoma are neoplasms with an intrinsically cystic growth pattern. Both tumor types should be included in a future WHO classification as novel entities owing to their characteristic features. Cysts and clear cell renal cell carcinoma frequently coexist within the kidneys of patients with von Hippel-Lindau disease. Sporadic clear cell renal cell carcinomas often contain cysts, usually as a minor component. Some clear cell renal cell carcinomas have prominent cysts, and multilocular cystic renal cell carcinoma is composed almost exclusively of cysts. Recent molecular findings suggest that clear cell renal cancer may develop through cyst-dependent and cyst-independent molecular pathways.

22 CFR 140.6 - Foreign government entities.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 22 Foreign Relations 1 2013-04-01 2013-04-01 false Foreign government entities. 140.6 Section 140... Enforcement § 140.6 Foreign government entities. (a) Determination Procedures. (1) The Country Narcotics... allegations that a key individual who is a senior government official of the host nation has been convicted of...

22 CFR 140.6 - Foreign government entities.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 22 Foreign Relations 1 2012-04-01 2012-04-01 false Foreign government entities. 140.6 Section 140... Enforcement § 140.6 Foreign government entities. (a) Determination Procedures. (1) The Country Narcotics... allegations that a key individual who is a senior government official of the host nation has been convicted of...

22 CFR 140.6 - Foreign government entities.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 22 Foreign Relations 1 2011-04-01 2011-04-01 false Foreign government entities. 140.6 Section 140... Enforcement § 140.6 Foreign government entities. (a) Determination Procedures. (1) The Country Narcotics... allegations that a key individual who is a senior government official of the host nation has been convicted of...

14 CFR Sec. 1-6 - Accounting entities.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 14 Aeronautics and Space 4 2010-01-01 2010-01-01 false Accounting entities. Sec. 1-6 Section 1-6... REGULATIONS UNIFORM SYSTEM OF ACCOUNTS AND REPORTS FOR LARGE CERTIFICATED AIR CARRIERS General Accounting Provisions Sec. 1-6 Accounting entities. (a) Separate accounting records shall be maintained for each air...

7 CFR 795.6 - Multiple individuals or other entities.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 7 2010-01-01 2010-01-01 false Multiple individuals or other entities. 795.6 Section... Multiple individuals or other entities. The rules in §§ 795.5 through 795.16 shall be used to determine whether certain multiple individuals or legal entities are to be treated as one person or as separate...

Time to Redefine the Intramammary Lymph Node as a Separate Entity?

PubMed

Green, M; Tafazal, H; Swati, B; Vidya, R

2018-04-17

The lymphatic drainage for the majority of primary breast tumours is to the axillary lymph nodes (ALNs). Some, however drain to the so-called extra-axillary basins, namely the internal mammary, supra- and infraclavicular regions. Another potential drainage route includes the intramammary lymph nodes (IMLNs). Current guidance suggests IMLNs should be considered as part of the axillary group, potentially affecting axillary management. However, due to evolution in imaging and advancement in technology, IMLNs may now be distinguished more accurately pre-operatively. There are currently no published guidelines for the management of IMLNs in the United Kingdom. The authors suggest that it is time to reclassify IMLNs as a separate focus of cancer and treat it as a separate entity. This article is protected by copyright. All rights reserved. © 2018 Wiley Periodicals, Inc.

Is naming faces different from naming objects? Semantic interference in a face- and object-naming task.

PubMed

Marful, Alejandra; Paolieri, Daniela; Bajo, M Teresa

2014-04-01

A current debate regarding face and object naming concerns whether they are equally vulnerable to semantic interference. Although some studies have shown similar patterns of interference, others have revealed different effects for faces and objects. In Experiment 1, we compared face naming to object naming when exemplars were presented in a semantically homogeneous context (grouped by their category) or in a semantically heterogeneous context (mixed) across four cycles. The data revealed significant slowing for both face and object naming in the homogeneous context. This semantic interference was explained as being due to lexical competition from the conceptual activation of category members. When focusing on the first cycle, a facilitation effect for objects but not for faces appeared. This result permits us to explain the previously observed discrepancies between face and object naming. Experiment 2 was identical to Experiment 1, with the exception that half of the stimuli were presented as face/object names for reading. Semantic interference was present for both face and object naming, suggesting that faces and objects behave similarly during naming. Interestingly, during reading, semantic interference was observed for face names but not for object names. This pattern is consistent with previous assumptions proposing the activation of a person identity during face name reading.

26 CFR 53.4965-2 - Covered tax-exempt entities.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 26 Internal Revenue 17 2011-04-01 2011-04-01 false Covered tax-exempt entities. 53.4965-2 Section... Covered tax-exempt entities. (a) In general. Under section 4965(c), the term “tax-exempt entity” refers to entities that are described in sections 501(c), 501(d), or 170(c) (other than the United States), Indian...

The Effect of Realistic Contexts on Ontological Judgments of Novel Entities.

PubMed

Van Reet, Jennifer; Pinkham, Ashley M; Lillard, Angeline S

2015-01-01

Although a great deal of research has focused on ontological judgments in preschoolers, very little has examined ontological judgments in older children. The present study asked 10-year-olds and adults (N = 94) to judge the reality status of known real, known imagined, and novel entities presented in simple and elaborate contexts and to explain their judgments. Although judgments were generally apt, participants were more likely to endorse imagined and novel entities when the entities were presented in elaborate contexts. When asked to explain their reasoning, participants at both ages cited firsthand experience for real entities and general knowledge for imagined entities. For novel entities, participants referred most to indirect experiences when entities were presented in simple contexts and to general knowledge when those entities were presented in elaborate contexts. These results suggest that rich contextual information continues to be an important influence on ontological judgments past the preschool years.

Enhancement of Chemical Entity Identification in Text Using Semantic Similarity Validation

PubMed Central

Grego, Tiago; Couto, Francisco M.

2013-01-01

With the amount of chemical data being produced and reported in the literature growing at a fast pace, it is increasingly important to efficiently retrieve this information. To tackle this issue text mining tools have been applied, but despite their good performance they still provide many errors that we believe can be filtered by using semantic similarity. Thus, this paper proposes a novel method that receives the results of chemical entity identification systems, such as Whatizit, and exploits the semantic relationships in ChEBI to measure the similarity between the entities found in the text. The method assigns a single validation score to each entity based on its similarities with the other entities also identified in the text. Then, by using a given threshold, the method selects a set of validated entities and a set of outlier entities. We evaluated our method using the results of two state-of-the-art chemical entity identification tools, three semantic similarity measures and two text window sizes. The method was able to increase precision without filtering a significant number of correctly identified entities. This means that the method can effectively discriminate the correctly identified chemical entities, while discarding a significant number of identification errors. For example, selecting a validation set with 75% of all identified entities, we were able to increase the precision by 28% for one of the chemical entity identification tools (Whatizit), maintaining in that subset 97% the correctly identified entities. Our method can be directly used as an add-on by any state-of-the-art entity identification tool that provides mappings to a database, in order to improve their results. The proposed method is included in a freely accessible web tool at www.lasige.di.fc.ul.pt/webtools/ice/. PMID:23658791

The Effect of Realistic Contexts on Ontological Judgments of Novel Entities

PubMed Central

Van Reet, Jennifer; Pinkham, Ashley M.; Lillard, Angeline S.

2014-01-01

Although a great deal of research has focused on ontological judgments in preschoolers, very little has examined ontological judgments in older children. The present study asked 10-year-olds and adults (N = 94) to judge the reality status of known real, known imagined, and novel entities presented in simple and elaborate contexts and to explain their judgments. Although judgments were generally apt, participants were more likely to endorse imagined and novel entities when the entities were presented in elaborate contexts. When asked to explain their reasoning, participants at both ages cited firsthand experience for real entities and general knowledge for imagined entities. For novel entities, participants referred most to indirect experiences when entities were presented in simple contexts and to general knowledge when those entities were presented in elaborate contexts. These results suggest that rich contextual information continues to be an important influence on ontological judgments past the preschool years. PMID:25914442

Massive ovarian oedema: a misleading clinical entity.

PubMed

Machairiotis, Nikolaos; Stylianaki, Aikaterini; Kouroutou, Paraskevi; Sarli, Polixeni; Alexiou, Nikolaos Konstantinos; Efthymiou, Elias; Maras, Athanasios; Alexiou, Nikolaos Georgios; Nikolaou, Spyridon Evaggelos; Courcoutsakis, Nikolaos; Papakonstantinou, Eleni; Zarogoulidis, Paul; Barbetakis, Nikolaos; Paliouras, Dimitrios; Gogakos, Apostolos; Machairiotis, Christodoulos

2016-02-03

Massive ovarian oedema is a rare non-neoplastic clinicopathologic entity has a higher incidence in women during their second and third life decade. The oedema can be presented in one or both ovaries as a result of partial intermittent torsion of the ovarian pedicle that interferes to the venal and lymphatic drainage of the ovary. We present a clinical case of a 16 year old with massive ovarian oedema and we performed a review of the literature. The pathophysiology of this entity is very complex. We tried to perform a complete review of the literature and focus on the complexity of this entity as far as its pathophysiological backround is concerned and as far as its clinical presentation is concerned. In conclusion, massive ovarian oedema is a rare, multi disease mimicking clinical entity, with an acute or progressive clinical presentation. It has also to be a part of our differential diagnosis in cases of acute abdominal pain and we have to try to treat her conservatively, in order to preserve fertility.

Framework for automatic information extraction from research papers on nanocrystal devices

PubMed Central

Yoshioka, Masaharu; Hara, Shinjiro; Newton, Marcus C

2015-01-01

Summary To support nanocrystal device development, we have been working on a computational framework to utilize information in research papers on nanocrystal devices. We developed an annotated corpus called “ NaDev” (Nanocrystal Device Development) for this purpose. We also proposed an automatic information extraction system called “NaDevEx” (Nanocrystal Device Automatic Information Extraction Framework). NaDevEx aims at extracting information from research papers on nanocrystal devices using the NaDev corpus and machine-learning techniques. However, the characteristics of NaDevEx were not examined in detail. In this paper, we conduct system evaluation experiments for NaDevEx using the NaDev corpus. We discuss three main issues: system performance, compared with human annotators; the effect of paper type (synthesis or characterization) on system performance; and the effects of domain knowledge features (e.g., a chemical named entity recognition system and list of names of physical quantities) on system performance. We found that overall system performance was 89% in precision and 69% in recall. If we consider identification of terms that intersect with correct terms for the same information category as the correct identification, i.e., loose agreement (in many cases, we can find that appropriate head nouns such as temperature or pressure loosely match between two terms), the overall performance is 95% in precision and 74% in recall. The system performance is almost comparable with results of human annotators for information categories with rich domain knowledge information (source material). However, for other information categories, given the relatively large number of terms that exist only in one paper, recall of individual information categories is not high (39–73%); however, precision is better (75–97%). The average performance for synthesis papers is better than that for characterization papers because of the lack of training examples for

Framework for automatic information extraction from research papers on nanocrystal devices.

PubMed

Dieb, Thaer M; Yoshioka, Masaharu; Hara, Shinjiro; Newton, Marcus C

2015-01-01

To support nanocrystal device development, we have been working on a computational framework to utilize information in research papers on nanocrystal devices. We developed an annotated corpus called " NaDev" (Nanocrystal Device Development) for this purpose. We also proposed an automatic information extraction system called "NaDevEx" (Nanocrystal Device Automatic Information Extraction Framework). NaDevEx aims at extracting information from research papers on nanocrystal devices using the NaDev corpus and machine-learning techniques. However, the characteristics of NaDevEx were not examined in detail. In this paper, we conduct system evaluation experiments for NaDevEx using the NaDev corpus. We discuss three main issues: system performance, compared with human annotators; the effect of paper type (synthesis or characterization) on system performance; and the effects of domain knowledge features (e.g., a chemical named entity recognition system and list of names of physical quantities) on system performance. We found that overall system performance was 89% in precision and 69% in recall. If we consider identification of terms that intersect with correct terms for the same information category as the correct identification, i.e., loose agreement (in many cases, we can find that appropriate head nouns such as temperature or pressure loosely match between two terms), the overall performance is 95% in precision and 74% in recall. The system performance is almost comparable with results of human annotators for information categories with rich domain knowledge information (source material). However, for other information categories, given the relatively large number of terms that exist only in one paper, recall of individual information categories is not high (39-73%); however, precision is better (75-97%). The average performance for synthesis papers is better than that for characterization papers because of the lack of training examples for characterization papers

Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications

PubMed Central

Masanz, James J; Ogren, Philip V; Zheng, Jiaping; Sohn, Sunghwan; Kipper-Schuler, Karin C; Chute, Christopher G

2010-01-01

We aim to build and evaluate an open-source natural language processing system for information extraction from electronic medical record clinical free-text. We describe and evaluate our system, the clinical Text Analysis and Knowledge Extraction System (cTAKES), released open-source at http://www.ohnlp.org. The cTAKES builds on existing open-source technologies—the Unstructured Information Management Architecture framework and OpenNLP natural language processing toolkit. Its components, specifically trained for the clinical domain, create rich linguistic and semantic annotations. Performance of individual components: sentence boundary detector accuracy=0.949; tokenizer accuracy=0.949; part-of-speech tagger accuracy=0.936; shallow parser F-score=0.924; named entity recognizer and system-level evaluation F-score=0.715 for exact and 0.824 for overlapping spans, and accuracy for concept mapping, negation, and status attributes for exact and overlapping spans of 0.957, 0.943, 0.859, and 0.580, 0.939, and 0.839, respectively. Overall performance is discussed against five applications. The cTAKES annotations are the foundation for methods and modules for higher-level semantic processing of clinical free-text. PMID:20819853

A natural language processing and geospatial clustering framework for harvesting local place names from geotagged housing advertisements

DOE PAGES

Hu, Yingjie; Mao, Huina; Mckenzie, Grant

2018-04-13

We report that local place names are frequently used by residents living in a geographic region. Such place names may not be recorded in existing gazetteers, due to their vernacular nature, relative insignificance to a gazetteer covering a large area (e.g. the entire world), recent establishment (e.g. the name of a newly-opened shopping center) or other reasons. While not always recorded, local place names play important roles in many applications, from supporting public participation in urban planning to locating victims in disaster response. In this paper, we propose a computational framework for harvesting local place names from geotagged housing advertisements.more » We make use of those advertisements posted on local-oriented websites, such as Craigslist, where local place names are often mentioned. The proposed framework consists of two stages: natural language processing (NLP) and geospatial clustering. The NLP stage examines the textual content of housing advertisements and extracts place name candidates. The geospatial stage focuses on the coordinates associated with the extracted place name candidates and performs multiscale geospatial clustering to filter out the non-place names. We evaluate our framework by comparing its performance with those of six baselines. Finally, we also compare our result with four existing gazetteers to demonstrate the not-yet-recorded local place names discovered by our framework.« less

A natural language processing and geospatial clustering framework for harvesting local place names from geotagged housing advertisements

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

Hu, Yingjie; Mao, Huina; Mckenzie, Grant

We report that local place names are frequently used by residents living in a geographic region. Such place names may not be recorded in existing gazetteers, due to their vernacular nature, relative insignificance to a gazetteer covering a large area (e.g. the entire world), recent establishment (e.g. the name of a newly-opened shopping center) or other reasons. While not always recorded, local place names play important roles in many applications, from supporting public participation in urban planning to locating victims in disaster response. In this paper, we propose a computational framework for harvesting local place names from geotagged housing advertisements.more » We make use of those advertisements posted on local-oriented websites, such as Craigslist, where local place names are often mentioned. The proposed framework consists of two stages: natural language processing (NLP) and geospatial clustering. The NLP stage examines the textual content of housing advertisements and extracts place name candidates. The geospatial stage focuses on the coordinates associated with the extracted place name candidates and performs multiscale geospatial clustering to filter out the non-place names. We evaluate our framework by comparing its performance with those of six baselines. Finally, we also compare our result with four existing gazetteers to demonstrate the not-yet-recorded local place names discovered by our framework.« less

26 CFR 301.7701(i)-4 - Special rules for certain entities.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 26 Internal Revenue 18 2010-04-01 2010-04-01 false Special rules for certain entities. 301.7701(i... rules for certain entities. (a) States and municipalities—(1) In general. Regardless of whether an entity satisfies any of the requirements of section 7701(i)(2)(A), an entity is not classified as a...

Rendering of Names of Corporate Bodies. Subject Analysis, With Special Reference to Social Sciences. Documentation Systems for Industry (8th Annual Seminar). Part 1: Papers.

ERIC Educational Resources Information Center

Documentation Research and Training Centre, Bangalore (India).

The four sections of the report cover the topics of cataloging, subject analysis, documentation systems for industry and the Documentation Research and Training Centre (DRTC) research report for 1970. The cataloging section covers the conflicts of cataloging, recall, corporate bodies, titles, publishers series and the entity name. The subject…

What's in a Name?-Consequences of Naming Non-Human Animals.

PubMed

Borkfelt, Sune

2011-01-19

The act of naming is among the most basic actions of language. Indeed, it is naming something that enables us to communicate about it in specific terms, whether the object named is human or non-human, animate or inanimate. However, naming is not as uncomplicated as we may usually think and names have consequences for the way we think about animals (human and non-human), peoples, species, places, things etc. Through a blend of history, philosophy and representational theory-and using examples from, among other things, the Bible, Martin Luther, colonialism/imperialism and contemporary ways of keeping and regarding non-human animals-this paper attempts to trace the importance of (both specific and generic) naming to our relationships with the non-human. It explores this topic from the naming of the animals in Genesis to the names given and used by scientists, keepers of companion animals, media etc. in our societies today, and asks the question of what the consequences of naming non-human animals are for us, for the beings named and for the power relations between our species and the non-human species and individuals we name.

Relation extraction for biological pathway construction using node2vec.

PubMed

Kim, Munui; Baek, Seung Han; Song, Min

2018-06-13

Systems biology is an important field for understanding whole biological mechanisms composed of interactions between biological components. One approach for understanding complex and diverse mechanisms is to analyze biological pathways. However, because these pathways consist of important interactions and information on these interactions is disseminated in a large number of biomedical reports, text-mining techniques are essential for extracting these relationships automatically. In this study, we applied node2vec, an algorithmic framework for feature learning in networks, for relationship extraction. To this end, we extracted genes from paper abstracts using pkde4j, a text-mining tool for detecting entities and relationships. Using the extracted genes, a co-occurrence network was constructed and node2vec was used with the network to generate a latent representation. To demonstrate the efficacy of node2vec in extracting relationships between genes, performance was evaluated for gene-gene interactions involved in a type 2 diabetes pathway. Moreover, we compared the results of node2vec to those of baseline methods such as co-occurrence and DeepWalk. Node2vec outperformed existing methods in detecting relationships in the type 2 diabetes pathway, demonstrating that this method is appropriate for capturing the relatedness between pairs of biological entities involved in biological pathways. The results demonstrated that node2vec is useful for automatic pathway construction.

29 CFR 1635.6 - Causing a covered entity to discriminate.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 29 Labor 4 2014-07-01 2014-07-01 false Causing a covered entity to discriminate. 1635.6 Section 1635.6 Labor Regulations Relating to Labor (Continued) EQUAL EMPLOYMENT OPPORTUNITY COMMISSION GENETIC INFORMATION NONDISCRIMINATION ACT OF 2008 § 1635.6 Causing a covered entity to discriminate. A covered entity...

29 CFR 1635.6 - Causing a covered entity to discriminate.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 29 Labor 4 2013-07-01 2013-07-01 false Causing a covered entity to discriminate. 1635.6 Section 1635.6 Labor Regulations Relating to Labor (Continued) EQUAL EMPLOYMENT OPPORTUNITY COMMISSION GENETIC INFORMATION NONDISCRIMINATION ACT OF 2008 § 1635.6 Causing a covered entity to discriminate. A covered entity...

29 CFR 1635.6 - Causing a covered entity to discriminate.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 29 Labor 4 2012-07-01 2012-07-01 false Causing a covered entity to discriminate. 1635.6 Section 1635.6 Labor Regulations Relating to Labor (Continued) EQUAL EMPLOYMENT OPPORTUNITY COMMISSION GENETIC INFORMATION NONDISCRIMINATION ACT OF 2008 § 1635.6 Causing a covered entity to discriminate. A covered entity...

29 CFR 1635.6 - Causing a covered entity to discriminate.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 29 Labor 4 2011-07-01 2011-07-01 false Causing a covered entity to discriminate. 1635.6 Section 1635.6 Labor Regulations Relating to Labor (Continued) EQUAL EMPLOYMENT OPPORTUNITY COMMISSION GENETIC INFORMATION NONDISCRIMINATION ACT OF 2008 § 1635.6 Causing a covered entity to discriminate. A covered entity...

7 CFR 652.23 - Certification process for private-sector entities.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 6 2010-01-01 2010-01-01 false Certification process for private-sector entities. 652... ASSISTANCE Certification § 652.23 Certification process for private-sector entities. (a) A private sector... individual basis as part of the private-sector entity's certification and ensures that the requirements set...

The emergence of overweight as a disease entity: measuring up normality.

PubMed

Jutel, Annemarie

2006-11-01

As Charles Rosenberg [(2002). The tyranny of diagnosis. The Milbank Quarterly, 80, 237-260] has recently written, clinical diagnosis contributes to imposing structure on cultural reality in a manner which is not unproblematic. A social power resides in the process of naming diseases-one, which legitimises concerns, explains reality, naturalises deviance and imposes status. But clinical entities are not static, as both the concerns of society, and the technological ability of practitioners change (what Rosenberg refers to as the "iatrogenesis of nosology"), so too do the range of labels available for identifying disease. In this paper, I argue that being "overweight," once predominantly an adjectival descriptor of corpulence, a physical sign or a symptom, and even, in some cultures, a sign of wealth and status, is undergoing the transformation to disease entity. I suggest that evidence of this is present in both the frequency and the way in which the term is being used by the media, the medical establishment and the laity. I argue that this change stems from the convergence of two particular phenomena. The first is the belief in the neutrality of quantification, and the objectivity that measurement brings to qualitative description. The second is the importance attributed to normative appearance in health. I discuss some of the implications of this evolution and its impact on health practices, including the exploitation of this purported disease state for commercial benefit.

Structured prediction models for RNN based sequence labeling in clinical text.

PubMed

Jagannatha, Abhyuday N; Yu, Hong

2016-11-01

Sequence labeling is a widely used method for named entity recognition and information extraction from unstructured natural language data. In clinical domain one major application of sequence labeling involves extraction of medical entities such as medication, indication, and side-effects from Electronic Health Record narratives. Sequence labeling in this domain, presents its own set of challenges and objectives. In this work we experimented with various CRF based structured learning models with Recurrent Neural Networks. We extend the previously studied LSTM-CRF models with explicit modeling of pairwise potentials. We also propose an approximate version of skip-chain CRF inference with RNN potentials. We use these methodologies for structured prediction in order to improve the exact phrase detection of various medical entities.

Structured prediction models for RNN based sequence labeling in clinical text

PubMed Central

Jagannatha, Abhyuday N; Yu, Hong

2016-01-01

Sequence labeling is a widely used method for named entity recognition and information extraction from unstructured natural language data. In clinical domain one major application of sequence labeling involves extraction of medical entities such as medication, indication, and side-effects from Electronic Health Record narratives. Sequence labeling in this domain, presents its own set of challenges and objectives. In this work we experimented with various CRF based structured learning models with Recurrent Neural Networks. We extend the previously studied LSTM-CRF models with explicit modeling of pairwise potentials. We also propose an approximate version of skip-chain CRF inference with RNN potentials. We use these methodologies1 for structured prediction in order to improve the exact phrase detection of various medical entities. PMID:28004040

Facility Name | Research Site Name | NREL

Science.gov Websites

ex ea commodo consequat. Images should have a width of 1746px - height can vary Capabilities Capability 1 Capability 2 Capability 3 Testing Facilities and Laboratories Laboratory Name Images should have a width of 768px - height can vary Download fact sheet Laboratory Name Images should have a width of

Multinodular and Vacuolating Neuronal Tumor: A Rare Seizure-associated Entity.

PubMed

Cathcart, Sahara J; Klug, Jeffrey R; Helvey, Jason T; L White, Matthew; Gard, Andrew P; McComb, Rodney D

2017-07-01

Multinodular and vacuolating neuronal tumor is a recently described seizure-associated entity with overlapping features of a malformative and neoplastic process. We report a case of multinodular and vacuolating neuronal tumor in a 29-year-old man with a history of recent headaches and complex partial seizures. Neuroimaging revealed a nonenhancing, T2 and T2 fluid-attenuated inversion recovery hyperintense multinodular lesion in the right temporal lobe. Lesional tissue demonstrated well-demarcated nodules of ganglioid cells with vacuolation of both the perikarya and the fibrillary neuropil-like background. The ganglioid cells showed weak cytoplasmic reactivity for synaptophysin and were nonreactive for neurofilament and chromogranin. CD34-positive stellate cells were present within the nodules. A 50-gene next-generation sequencing panel did not identify any somatic mutations in genomic DNA extracted from the tumor.

KneeTex: an ontology-driven system for information extraction from MRI reports.

PubMed

Spasić, Irena; Zhao, Bo; Jones, Christopher B; Button, Kate

2015-01-01

In the realm of knee pathology, magnetic resonance imaging (MRI) has the advantage of visualising all structures within the knee joint, which makes it a valuable tool for increasing diagnostic accuracy and planning surgical treatments. Therefore, clinical narratives found in MRI reports convey valuable diagnostic information. A range of studies have proven the feasibility of natural language processing for information extraction from clinical narratives. However, no study focused specifically on MRI reports in relation to knee pathology, possibly due to the complexity of knee anatomy and a wide range of conditions that may be associated with different anatomical entities. In this paper we describe KneeTex, an information extraction system that operates in this domain. As an ontology-driven information extraction system, KneeTex makes active use of an ontology to strongly guide and constrain text analysis. We used automatic term recognition to facilitate the development of a domain-specific ontology with sufficient detail and coverage for text mining applications. In combination with the ontology, high regularity of the sublanguage used in knee MRI reports allowed us to model its processing by a set of sophisticated lexico-semantic rules with minimal syntactic analysis. The main processing steps involve named entity recognition combined with coordination, enumeration, ambiguity and co-reference resolution, followed by text segmentation. Ontology-based semantic typing is then used to drive the template filling process. We adopted an existing ontology, TRAK (Taxonomy for RehAbilitation of Knee conditions), for use within KneeTex. The original TRAK ontology expanded from 1,292 concepts, 1,720 synonyms and 518 relationship instances to 1,621 concepts, 2,550 synonyms and 560 relationship instances. This provided KneeTex with a very fine-grained lexico-semantic knowledge base, which is highly attuned to the given sublanguage. Information extraction results were evaluated

14 CFR Sec. 2-2 - Basis of allocation between entities.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 14 Aeronautics and Space 4 2011-01-01 2011-01-01 false Basis of allocation between entities. Sec... AIR CARRIERS General Accounting Provisions Sec. 2-2 Basis of allocation between entities. (a) The... the air carrier, as well as each transport entity and organizational division of the air carrier for...

12 CFR 1227.4 - Regulated entity reports on covered misconduct.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 12 Banks and Banking 10 2014-01-01 2014-01-01 false Regulated entity reports on covered misconduct... COUNTERPARTY PROGRAM General § 1227.4 Regulated entity reports on covered misconduct. (a) General. A regulated... the past three (3) years has engaged in covered misconduct. A regulated entity is aware of covered...

17 CFR Appendix A to Part 420 - Separate Reporting Entity

Code of Federal Regulations, 2010 CFR

2010-04-01

... reporting rules; (3) Decisions related to the purchase, sale or retention of Treasury securities must be made by employees of such entity(ies). Employees of such entity(ies) who make decisions to purchase or...) The records of such entity(ies) related to the ownership, financing, purchase and sale of Treasury...

78 FR 45051 - Unincorporated Business Entities; Effective Date

Federal Register 2010, 2011, 2012, 2013, 2014

2013-07-26

... under State law for certain business activities. In accordance with the law, the effective date of the...) institutions' use of unincorporated business entities (UBEs) organized under State law for certain business... business entities, such as unincorporated business trusts, organized under State law. The final rule does...

17 CFR 49.6 - Registration of successor entities.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 17 Commodity and Securities Exchanges 1 2012-04-01 2012-04-01 false Registration of successor entities. 49.6 Section 49.6 Commodity and Securities Exchanges COMMODITY FUTURES TRADING COMMISSION SWAP DATA REPOSITORIES § 49.6 Registration of successor entities. (a) In the event of a corporate...

17 CFR 49.6 - Registration of successor entities.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 17 Commodity and Securities Exchanges 1 2013-04-01 2013-04-01 false Registration of successor entities. 49.6 Section 49.6 Commodity and Securities Exchanges COMMODITY FUTURES TRADING COMMISSION SWAP DATA REPOSITORIES § 49.6 Registration of successor entities. (a) In the event of a corporate...

15 CFR 744.10 - Restrictions on certain entities in Russia.