1. Articles in category: NER

    73-96 of 415 « 1 2 3 4 5 6 7 ... 16 17 18 »
    1. Learning multiple distributed prototypes of semantic categories for named entity recognition.

      Learning multiple distributed prototypes of semantic categories for named entity recognition.

      Learning multiple distributed prototypes of semantic categories for named entity recognition.

      Int J Data Min Bioinform. 2015;13(4):395-411

      Authors: Henriksson A

      Abstract The scarcity of large labelled datasets comprising clinical text that can be exploited within the paradigm of supervised machine learning creates barriers for the secondary use of data from electronic health records.

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    2. Named Entity Recognition with Bidirectional LSTM-CNNs. (arXiv:1511.08308v1 [cs.CL])

      Named entity recognition is a challenging task that has traditionally required large amounts of knowledge in the form of feature engineering and lexicons to achieve high performance. In this paper, we present a novel neural network architecture that automatically detects word- and character-level features using a hybrid bidirectional LSTM and CNN architecture, eliminating the need for most feature engineering.

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      Mentions: CNN
    3. Semi-supervised Bootstrapping approach for Named Entity Recognition. (arXiv:1511.06833v1 [cs.CL])

      The aim of Named Entity Recognition (NER) is to identify references of named entities in unstructured documents, and to classify them into pre-defined semantic categories. NER often aids from added background knowledge in the form of gazetteers. However using such a collection does not deal with name variants and cannot resolve ambiguities associated in identifying the entities in context and associating them with predefined categories.

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    4. A Study of Active Learning Methods for Named Entity Recognition in Clinical Text.

      A Study of Active Learning Methods for Named Entity Recognition in Clinical Text.

      A Study of Active Learning Methods for Named Entity Recognition in Clinical Text.

      J Biomed Inform. 2015 Sep 15;

      Authors: Chen Y, Lasko TA, Mei Q, Denny JC, Xu H

      Abstract OBJECTIVES: Named entity recognition (NER), a sequential labeling task, is one of the fundamental tasks for building clinical natural language processing (NLP) systems.

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      Mentions: Denny JC Chen Y NLP
    5. Architecture for responding to a visual query

      A visual query such as a photograph, a screen shot, a scanned image, a video frame, or an image created by a content authoring application is submitted to a visual query search system. The search system processes the visual query by sending it to a plurality of parallel search systems, each implementing a distinct visual query search process. These parallel search systems may include but are not limited to optical character recognition (OCR), facial recognition, product recognition, bar code recognition, object-or-object-category recognition, named entity recognition, and color recognition. Then at least one search result is sent to the client system ...

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

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

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

      J Biomed Semantics. 2015;6:34

      Authors: Spasić I, Zhao B, Jones CB, Button K

      Abstract BACKGROUND: 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.

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

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

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

      J Biomed Inform. 2015 Aug 21;

      Authors: Urbain J

      Abstract 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.

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

      1School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA. 2Center for Medical Informatics, Peking University, Beijing, China. 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.

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      Mentions: Beijing China Houston
    9. Challenges in clinical natural language processing for automated disorder normalization.

      1National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, United States. Electronic address: robert.leaman@nih.gov. 2National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, United States. Electronic address: ritu.khare@nih.gov.

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    10. Anatomical entity recognition with a hierarchical framework augmented by external resources.

      1State Key Laboratory of Software Development Environment, Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education, Beihang University, Beijing, China; Microsoft Research Asia, Beijing, China. 2State Key Laboratory of Software Development Environment, Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education, Beihang University, Beijing, China.

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    11. Complex epilepsy phenotype extraction from narrative clinical discharge summaries.

      1Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA. 2Division of Medical Informatics, Case Western Reserve University, Cleveland, OH 44106, USA. 3Department of Neurology, Case Western Reserve University, Cleveland, OH 44106, USA.

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    12. Improving named entity recognition accuracy for gene and protein in biomedical text literature.

      The task of recognising biomedical named entities in natural language documents called biomedical Named Entity Recognition (NER) is the focus of many researchers due to complex nature of such texts. This complexity includes the issues of character-level, word-level and word order variations. In this study, an approach for recognising gene and protein names that handles the above issues is proposed.

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      Mentions: Genia
    13. Automated Misspelling Detection and Correction in Clinical Free-Text Records.

      Automated Misspelling Detection and Correction in Clinical Free-Text Records.

      Automated Misspelling Detection and Correction in Clinical Free-Text Records.

      J Biomed Inform. 2015 Apr 24;

      Authors: Lai KH, Topaz M, Goss FR, Zhou L

      Abstract Accurate electronic health records are important for clinical care and research as well as ensuring patient safety. It is crucial for misspelled words to be corrected in order to ensure that medical records are interpreted correctly. This paper describes the development of a spelling correction system for medical text.

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    14. Named entity recognition in query

      Named Entity Recognition in Query (NERQ) involves detection of a named entity in a given query and classification of the named entity into one or more predefined classes. The predefined classes may be based on a predefined taxonomy. A probabilistic approach may be taken to detecting and classifying named entities in queries, the approach using either query log data or click through data and Weakly Supervised Latent Dirichlet Allocation (WS-LDA) to construct and train a topic model.

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    15. Enhancing medical named entity recognition with an extended segment representation technique.

      Enhancing medical named entity recognition with an extended segment representation technique.

      Comput Methods Programs Biomed. 2015 Mar 4;

      Authors: Keretna S, Lim CP, Creighton D, Shaban KB

      Abstract OBJECTIVE: The objective of this paper is to formulate an extended segment representation (SR) technique to enhance named entity recognition (NER) in medical applications. METHODS: An extension to the IOBES (Inside/Outside/Begin/End/Single) SR technique is formulated.

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      Mentions: NER LIM
    16. Natural language processing methods for enhancing geographic metadata for phylogeography of zoonotic viruses.

      Natural language processing methods for enhancing geographic metadata for phylogeography of zoonotic viruses.

      AMIA Jt Summits Transl Sci Proc. 2014;2014:102-11

      Authors: Tahsin T, Beard R, Rivera R, Lauder R, Wallstrom G, Scotch M, Gonzalez G

      Abstract Zoonotic viruses represent emerging or re-emerging pathogens that pose significant public health threats throughout the world.

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    17. Method and apparatus for named entity recognition in chinese character strings utilizing an optimal path in a named entity candidate lattice

      The present invention provides a method for recognizing a named entity included in natural language, comprising the steps of: performing gradual parsing model training with the natural language to obtain a classification model; performing gradual parsing and recognition according to the obtained classification model to obtain information on positions and types of candidate named entities; performing a refusal recognition process for the candidate named entities; and generating a candidate named entity lattice from the refusal-recognition-processed candidate named entities, and searching for a optimal path. The present invention uses a one-class classifier to score or evaluate these results to obtain the ...

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

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

      J Biomed Inform. 2014 Feb;47:139-52

      Authors: Chae J, Jung Y, Lee T, Jung S, Huh C, Kim G, Kim H, Oh H

      Abstract 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.

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    19. Mapping biological entities using the longest approximately common prefix method.

      Mapping biological entities using the longest approximately common prefix method.

      Mapping biological entities using the longest approximately common prefix method.

      BMC Bioinformatics. 2014;15:187

      Authors: Rudniy A, Song M, Geller J

      Abstract BACKGROUND: The significant growth in the volume of electronic biomedical data in recent decades has pointed to the need for approximate string matching algorithms that can expedite tasks such as named entity recognition, duplicate detection, terminology integration, and spelling correction.

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    20. LabeledIn: Cataloging labeled indications for human drugs.

      LabeledIn: Cataloging labeled indications for human drugs.

      J Biomed Inform. 2014 Aug 23;

      Authors: Khare R, Li J, Lu Z

      Abstract Drug-disease treatment relationships, i.e., which drug(s) are indicated to treat which disease(s), are among the most frequently sought information in PubMed®. Such information is useful for feeding the Google Knowledge Graph, designing computational methods to predict novel drug indications, and validating clinical information in EMRs.

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    21. Ontotext Delivers Semantic Publishing Solutions to the World’s Largest Media & Publishing Companies

      Ontotext Delivers Semantic Publishing Solutions to the World’s Largest Media & Publishing Companies

      Ontotext Media & Publishing delivers semantic publishing solutions to the world’s largest media and publishing companies including automated content enrichment, data management, content and user analytics and natural language processing. Recently, Ontotext Media and Publishing has been enhanced to include contextually-aware reading recommendations based on content and user behavior, delivering ...

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    22. Generalising semantic category disambiguation with large lexical resources for fun and profit.

      Generalising semantic category disambiguation with large lexical resources for fun and profit.

      J Biomed Semantics. 2014;5:26

      Authors: Stenetorp P, Pyysalo S, Ananiadou S, Tsujii J

      Abstract BACKGROUND: Semantic Category Disambiguation (SCD) is the task of assigning the appropriate semantic category to given spans of text from a fixed set of candidate categories, for example Protein to "Fibrin".

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    23. Text mining of cancer-related information: Review of current status and future directions.

      Text mining of cancer-related information: Review of current status and future directions.

      Int J Med Inform. 2014 Jun 24;

      Authors: Spasić I, Livsey J, Keane JA, Nenadić G

      Abstract PURPOSE: This paper reviews the research literature on text mining (TM) with the aim to find out (1) which cancer domains have been the subject of TM efforts, (2) which knowledge resources can support TM of cancer-related information and (3) to what extent systems that rely on knowledge and computational methods can convert text data into useful clinical information. These questions were used to determine the current state of the art ...

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    73-96 of 415 « 1 2 3 4 5 6 7 ... 16 17 18 »
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