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1. Agarwal S, Yu H: Automatically classifying sentences in full-text biomedical articles into introduction, methods, results and discussion. Summit Transl Bioinform; 2009 Mar 01;2009:6-10
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  • [Source] The source of this record is MEDLINE®, a database of the U.S. National Library of Medicine.
  • [Title] Automatically classifying sentences in full-text biomedical articles into introduction, methods, results and discussion.
  • BIOMEDICAL TEXTS CAN BE TYPICALLY REPRESENTED BY FOUR RHETORICAL CATEGORIES: introduction, methods, results and discussion (IMRAD).
  • Classifying sentences into these categories can benefit many other text-mining tasks.
  • Although many studies have applied approaches to automatically classify sentences in MEDLINE abstracts into the IMRAD categories, few have explored the classification of sentences that appear in full-text biomedical articles.
  • We explored different approaches to automatically classify a sentence in a full-text biomedical article into the IMRAD categories.
  • Our best system is a support vector machine classifier that achieved 81.30% accuracy, which is significantly higher than baseline systems.

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  • (PMID = 21347163.001).
  • [Journal-full-title] Summit on translational bioinformatics
  • [ISO-abbreviation] Summit Transl Bioinform
  • [Language] ENG
  • [Grant] United States / NLM NIH HHS / LM / R01 LM009836; United States / NCRR NIH HHS / RR / R21 RR024933
  • [Publication-type] Journal Article
  • [Publication-country] United States
  • [Other-IDs] NLM/ PMC3041564
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