1. Articles in category: NER

    25-48 of 374 « 1 2 3 4 5 ... 14 15 16 »
    1. Extraction of pharmacokinetic evidence of drug-drug interactions from the literature.

      Extraction of pharmacokinetic evidence of drug-drug interactions from the literature.

      Extraction of pharmacokinetic evidence of drug-drug interactions from the literature.

      PLoS One. 2015;10(5):e0122199

      Authors: Kolchinsky A, Lourenço A, Wu HY, Li L, Rocha LM

      Abstract 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.

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      Mentions: Rocha Li L MCC
    2. Techniques for updating a partial dialog state

      Embodiments provide for tracking a partial dialog state as part of managing a dialog state space, but the embodiments are not so limited. A method of an embodiment jointly models partial state update and named entity recognition using a sequence-based classification or other model, wherein recognition of named entities and a partial state update can be performed in a single processing stage at runtime to generate a distribution over partial dialog states. A system of an embodiment is configured to generate a distribution over partial dialog states at runtime in part using a sequence classification decoding or other algorithm to ...

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    3. SimConcept: a hybrid approach for simplifying composite named entities in biomedical text.

      SimConcept: a hybrid approach for simplifying composite named entities in biomedical text.

      SimConcept: a hybrid approach for simplifying composite named entities in biomedical text.

      IEEE J Biomed Health Inform. 2015 Jul;19(4):1385-91

      Authors: Wei CH, Leaman R, Lu Z

      Abstract One particular challenge in biomedical named entity recognition (NER) and normalization is the identification and resolution of composite named entities, where a single span refers to more than one concept (e.g., BRCA1/2).

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    4. Neural Architectures for Named Entity Recognition. (arXiv:1603.01360v1 [cs.CL])

      State-of-the-art named entity recognition systems rely heavily on hand-crafted features and domain-specific knowledge in order to learn effectively from the small, supervised training corpora that are available. In this paper, we introduce two new neural architectures---one based on bidirectional LSTMs and conditional random fields, and the other that constructs and labels segments using a transition-based approach inspired by shift-reduce parsers.

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    5. GeoTextTagger: High-Precision Location Tagging of Textual Documents using a Natural Language Processing Approach. (arXiv:1601.05893v1 [cs.AI])

      Location tagging, also known as geotagging or geolocation, is the process of assigning geographical coordinates to input data. In this paper we present an algorithm for location tagging of textual documents. Our approach makes use of previous work in natural language processing by using a state-of-the-art part-of-speech tagger and named entity recognizer to find blocks of text which may refer to locations.

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    6. Automatic collection of speaker name pronunciations

      An audio stream is segmented into a plurality of time segments using speaker segmentation and recognition (SSR), with each time segment corresponding to the speaker's name, producing an SSR transcript. The audio stream is transcribed into a plurality of word regions using automatic speech recognition (ASR), with each of the word regions having a measure of the confidence in the accuracy of the translation, producing an ASR transcript. Word regions with a relatively low confidence in the accuracy of the translation are identified. The low confidence regions are filtered using named entity recognition (NER) rules to identify low confidence ...

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      Mentions: ASR NER
    7. 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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    8. 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
    9. 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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    10. 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
    11. 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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    12. 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
    13. 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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    14. 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
    15. 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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    16. 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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    17. 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
    18. 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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    19. 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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    20. 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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    21. 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
    22. 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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    25-48 of 374 « 1 2 3 4 5 ... 14 15 16 »
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