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. 2009 Dec 1;25(23):3174-80.
doi: 10.1093/bioinformatics/btp548. Epub 2009 Sep 25.

Automatically Classifying Sentences in Full-Text Biomedical Articles Into Introduction, Methods, Results and Discussion

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Free PMC article

Automatically Classifying Sentences in Full-Text Biomedical Articles Into Introduction, Methods, Results and Discussion

Shashank Agarwal et al. Bioinformatics. .
Free PMC article

Abstract

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 naïve 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/.

Figures

Fig. 1.
Fig. 1.
Accuracy of the Man-All classifier for different sized gold standard subsets.

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