Literature DB >> 22195078

Parenthetically speaking: classifying the contents of parentheses for text mining.

K Bretonnel Cohen1, Thomas Christiansen, Lawrence E Hunter.   

Abstract

The contents of parentheses in biomedical text have many potential uses in text mining applications. However, making use of them requires the ability to determine what class of contents they are. A system that automatically classifies parenthesized text into one of 20 categories is presented and evaluated here. It performs at a micro-averaged accuracy of 68% and a macro-averaged accuracy of 60% on an annotated corpus. The application is available as a Java class and as a Perl module.

Mesh:

Year:  2011        PMID: 22195078      PMCID: PMC3243264     

Source DB:  PubMed          Journal:  AMIA Annu Symp Proc        ISSN: 1559-4076


  12 in total

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5.  The structural and content aspects of abstracts versus bodies of full text journal articles are different.

Authors:  K Bretonnel Cohen; Helen L Johnson; Karin Verspoor; Christophe Roeder; Lawrence E Hunter
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7.  The textual characteristics of traditional and Open Access scientific journals are similar.

Authors:  Karin Verspoor; K Bretonnel Cohen; Lawrence Hunter
Journal:  BMC Bioinformatics       Date:  2009-06-15       Impact factor: 3.169

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Journal:  BMC Bioinformatics       Date:  2009-09-24       Impact factor: 3.169

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  5 in total

1.  Concept annotation in the CRAFT corpus.

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4.  Testing software's changing features with environment-driven abstraction identification.

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5.  A corpus of full-text journal articles is a robust evaluation tool for revealing differences in performance of biomedical natural language processing tools.

Authors:  Karin Verspoor; Kevin Bretonnel Cohen; Arrick Lanfranchi; Colin Warner; Helen L Johnson; Christophe Roeder; Jinho D Choi; Christopher Funk; Yuriy Malenkiy; Miriam Eckert; Nianwen Xue; William A Baumgartner; Michael Bada; Martha Palmer; Lawrence E Hunter
Journal:  BMC Bioinformatics       Date:  2012-08-17       Impact factor: 3.169

  5 in total

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