1. Articles in category: NER

    49-72 of 371 « 1 2 3 4 5 6 ... 14 15 16 »
    1. 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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    2. 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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    3. 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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    4. 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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    5. 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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    6. 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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    7. 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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    8. 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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    9. 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
    10. 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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    11. 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.
    12. 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
    13. 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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    14. 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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    15. 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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    16. 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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    17. Recognizing clinical entities in hospital discharge summaries using Structural Support Vector Machines with word representation features.

      Recognizing clinical entities in hospital discharge summaries using Structural Support Vector Machines with word representation features. BMC Med Inform Decis Mak. 2013;13 Suppl 1:S1 Authors: Tang B, Cao H, Wu Y, Jiang M, Xu H Abstract BACKGROUND: Named entity recognition (NER) is an important task in clinical natural language processing (NLP) research. Machine learning (ML) based NER methods have shown good performance in recognizing entities in clinical text. Algorithms and features are two important factors that largely affect the performance of ML-based NER systems. Conditional Random Fields (CRFs), a sequential labelling algorithm, and Support Vector Machines (SVMs), which ...
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    18. Extracting drug indication information from structured product labels using natural language processing.

      Extracting drug indication information from structured product labels using natural language processing. J Am Med Inform Assoc. 2013 Mar 9; Authors: Fung KW, Jao CS, Demner-Fushman D Abstract OBJECTIVE: To extract drug indications from structured drug labels and represent the information using codes from standard medical terminologies. MATERIALS AND METHODS: We used MetaMap and other publicly available resources to extract information from the indications section of drug labels. Drugs and indications were encoded by RxNorm and UMLS identifiers respectively. A sample was manually reviewed. We also compared the results with two independent information sources: National Drug File-Reference Terminology and the ...
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    19. ChemSpot: a hybrid system for chemical named entity recognition.

      Related Articles ChemSpot: a hybrid system for chemical named entity recognition. Bioinformatics. 2012 Jun 15;28(12):1633-40 Authors: Rocktäschel T, Weidlich M, Leser U Abstract MOTIVATION: The accurate identification of chemicals in text is important for many applications, including computer-assisted reconstruction of metabolic networks or retrieval of information about substances in drug development. But due to the diversity of naming conventions and traditions for such molecules, this task is highly complex and should be supported by computational tools. RESULTS: We present ChemSpot, a named entity recognition (NER) tool for identifying mentions of chemicals in natural language texts, including trivial ...
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    49-72 of 371 « 1 2 3 4 5 6 ... 14 15 16 »
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