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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...

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Detalles Bibliográficos
Autores principales: Agarwal, Shashank, Yu, Hong
Formato: Texto
Lenguaje:English
Publicado: American Medical Informatics Association 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3041564/
https://www.ncbi.nlm.nih.gov/pubmed/21347163
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author Agarwal, Shashank
Yu, Hong
author_facet Agarwal, Shashank
Yu, Hong
author_sort Agarwal, Shashank
collection PubMed
description 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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spelling pubmed-30415642011-02-23 Automatically Classifying Sentences in Full-Text Biomedical Articles into Introduction, Methods, Results and Discussion Agarwal, Shashank Yu, Hong Summit on Translat Bioinforma Articles 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. American Medical Informatics Association 2009-03-01 /pmc/articles/PMC3041564/ /pubmed/21347163 Text en ©2009 AMIA - All rights reserved. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose
spellingShingle Articles
Agarwal, Shashank
Yu, Hong
Automatically Classifying Sentences in Full-Text Biomedical Articles into Introduction, Methods, Results and Discussion
title Automatically Classifying Sentences in Full-Text Biomedical Articles into Introduction, Methods, Results and Discussion
title_full Automatically Classifying Sentences in Full-Text Biomedical Articles into Introduction, Methods, Results and Discussion
title_fullStr Automatically Classifying Sentences in Full-Text Biomedical Articles into Introduction, Methods, Results and Discussion
title_full_unstemmed Automatically Classifying Sentences in Full-Text Biomedical Articles into Introduction, Methods, Results and Discussion
title_short Automatically Classifying Sentences in Full-Text Biomedical Articles into Introduction, Methods, Results and Discussion
title_sort automatically classifying sentences in full-text biomedical articles into introduction, methods, results and discussion
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3041564/
https://www.ncbi.nlm.nih.gov/pubmed/21347163
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