1. Articles in category: NER

    49-72 of 374 « 1 2 3 4 5 6 ... 14 15 16 »
    1. 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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    2. 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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    3. 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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    4. 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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    5. 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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    6. 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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    7. 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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    8. Formative evaluation of ontology learning methods for entity discovery by using existing ontologies as reference standards.

      Formative evaluation of ontology learning methods for entity discovery by using existing ontologies as reference standards.

      Methods Inf Med. 2013;52(4):308-16

      Authors: Liu K, Mitchell KJ, Chapman WW, Savova GK, Sioutos N, Rubin DL, Crowley RS

      Abstract OBJECTIVE: Developing a two-step method for formative evaluation of statistical Ontology Learning (OL) algorithms that leverages existing biomedical ontologies as reference standards.

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    9. Automated Detection of Ambiguity in BI-RADS Assessment Categories in Mammography Reports.

      Automated Detection of Ambiguity in BI-RADS Assessment Categories in Mammography Reports.

      Stud Health Technol Inform. 2014;197:35-9

      Authors: Bozkurt S, Rubin D

      Abstract An unsolved challenge in biomedical natural language processing (NLP) is detecting ambiguities in the reports that can help physicians to improve report clarity.

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    10. Evaluating word representation features in biomedical named entity recognition tasks.

      Evaluating word representation features in biomedical named entity recognition tasks.

      Biomed Res Int. 2014;2014:240403

      Authors: Tang B, Cao H, Wang X, Chen Q, Xu H

      Abstract Biomedical Named Entity Recognition (BNER), which extracts important entities such as genes and proteins, is a crucial step of natural language processing in the biomedical domain. Various machine learning-based approaches have been applied to BNER tasks and showed good performance.

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    11. Automatic recognition of disorders, findings, pharmaceuticals and body structures from clinical text: An annotation and machine learning study.

      Automatic recognition of disorders, findings, pharmaceuticals and body structures from clinical text: An annotation and machine learning study.

      J Biomed Inform. 2014 Feb 4;

      Authors: Skeppstedt M, Kvist M, Nilsson GH, Dalianis H

      Abstract Automatic recognition of clinical entities in the narrative text of health records is useful for constructing applications for documentation of patient care, as well as for secondary usage in the form of medical knowledge extraction.

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    12. A comprehensive study of named entity recognition in Chinese clinical text.

      A comprehensive study of named entity recognition in Chinese clinical text.

      J Am Med Inform Assoc. 2013 Dec 17;

      Authors: Lei J, Tang B, Lu X, Gao K, Jiang M, Xu H

      Abstract OBJECTIVE: Named entity recognition (NER) is one of the fundamental tasks in natural language processing. In the medical domain, there have been a number of studies on NER in English clinical notes; however, very limited NER research has been carried out on clinical notes written in Chinese.

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      Mentions: China SVM CRF
    13. Evaluating the impact of pre-annotation on annotation speed and potential bias: natural language processing gold standard development for clinical named entity recognition in clinical trial announcements.

      Evaluating the impact of pre-annotation on annotation speed and potential bias: natural language processing gold standard development for clinical named entity recognition in clinical trial announcements.

      J Am Med Inform Assoc. 2013 Sep 3;

      Authors: Lingren T, Deleger L, Molnar K, Zhai H, Meinzen-Derr J, Kaiser M, Stoutenborough L, Li Q, Solti I

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    14. Unsupervised Biomedical Named Entity Recognition: Experiments with Clinical and Biological Texts.

      Unsupervised Biomedical Named Entity Recognition: Experiments with Clinical and Biological Texts.

      J Biomed Inform. 2013 Aug 15;

      Authors: Zhang S, Elhadad N

      Abstract Named entity recognition is a crucial component of biomedical natural language processing, enabling information extraction and ultimately reasoning over and knowledge discovery from text. Much progress has been made in the design of rule-based and supervised tools, but they are often genre and task dependent.

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      Mentions: Elsevier Inc.
    15. Joint segmentation and named entity recognition using dual decomposition in Chinese discharge summaries.

      Joint segmentation and named entity recognition using dual decomposition in Chinese discharge summaries.

      J Am Med Inform Assoc. 2013 Aug 9;

      Authors: Xu Y, Wang Y, Liu T, Liu J, Fan Y, Qian Y, Tsujii J, Chang EI

      Abstract OBJECTIVE: In this paper, we focus on three aspects: (1) to annotate a set of standard corpus in Chinese discharge summaries; (2) to perform word segmentation and named entity recognition in the above corpus; (3) to build a joint model that performs word segmentation and named entity recognition. DESIGN: Two independent systems of word segmentation and named entity recognition were built ...

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      Mentions: Tsujii J Liu J Wang Y
    16. Boundary adjustment of events in clinical named entity recognition. (arXiv:1308.1004v1 [cs.CL])

      The problem of named entity recognition in the medical/clinical domain has gained increasing attention do to its vital role in a wide range of clinical decision support applications. The identification of complete and correct term span is vital for further knowledge synthesis (e.g., coding/mapping concepts thesauruses and classification standards).

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    17. A Comparison of Named Entity Recognition Tools Applied to Biographical Texts. (arXiv:1308.0661v1 [cs.IR])

      Named entity recognition (NER) is a popular domain of natural language processing. For this reason, many tools exist to perform this task. Amongst other points, they differ in the processing method they rely upon, the entity types they can detect, the nature of the text they can handle, and their input/output formats. This makes it difficult for a user to select an appropriate NER tool for a specific situation. In this article, we try to answer this question in the context of biographic texts.

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    18. Using Empirically Constructed Lexical Resources for Named Entity Recognition.

      Using Empirically Constructed Lexical Resources for Named Entity Recognition. Biomed Inform Insights. 2013;6(Suppl 1):17-27 Authors: Jonnalagadda S, Cohen T, Wu S, Liu H, Gonzalez G Abstract Because of privacy concerns and the expense involved in creating an annotated corpus, the existing small-annotated corpora might not have sufficient examples for learning to statistically extract all the named-entities precisely. In this work, we evaluate what value may lie in automatically generated features based on distributional semantics when using machine-learning named entity recognition (NER). The features we generated and experimented with include n-nearest words, support vector machine (SVM)-regions, and ...
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    19. IMS Health Advances Cloud-Based Social Media Analytics with Acquisition of Semantelli

      DANBURY, Conn.--(BUSINESS WIRE)--IMS Health today announced the acquisition of Semantelli Corporation, a Bridgewater, N.J.-based social media analytics company, to extend its marketing and consumer engagement capabilities for healthcare organizations around the world. Semantelli offers clients a robust set of cloud-based tools that automate the collection of healthcare-specific social media ...
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    49-72 of 374 « 1 2 3 4 5 6 ... 14 15 16 »
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