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1. Agarwal S, Yu H: Automatically classifying sentences in full-text biomedical articles into Introduction, Methods, Results and Discussion. Bioinformatics; 2009 Dec 1;25(23):3174-80
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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 different approaches for automatically classifying sentences in MEDLINE abstracts into the IMRAD categories, few have explored the classification of sentences that appear in full-text biomedical articles.
  • We first evaluated whether sentences in full-text biomedical articles could be reliably annotated into the IMRAD format and then explored different approaches for automatically classifying these sentences into the IMRAD categories.
  • Our results show an overall annotation agreement of 82.14% with a Kappa score of 0.756.
  • The best classification system is a multinomial na├»ve Bayes classifier trained on manually annotated data that achieved 91.95% accuracy and an average F-score of 91.55%, which is significantly higher than baseline systems.
  • A web version of this system is available online at-http://wood.ims.uwm.edu/full_text_classifier/.

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  • (PMID = 19783830.001).
  • [ISSN] 1367-4811
  • [Journal-full-title] Bioinformatics (Oxford, England)
  • [ISO-abbreviation] Bioinformatics
  • [Language] ENG
  • [Grant] United States / NLM NIH HHS / LM / R01 LM009836-01A1; United States / NIGMS NIH HHS / GM / R01 GM095476; United States / NLM NIH HHS / LM / 5R01LM009836-02; United States / NLM NIH HHS / LM / R01 LM009836; United States / NLM NIH HHS / LM / LM009836-01A1; United States / NCRR NIH HHS / RR / 1R21RR024933-01A1
  • [Publication-type] Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't
  • [Publication-country] England
  • [Other-IDs] NLM/ PMC2913661
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