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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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Formato: | Texto |
Lenguaje: | English |
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American Medical Informatics Association
2009
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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. |
format | Text |
id | pubmed-3041564 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2009 |
publisher | American Medical Informatics Association |
record_format | MEDLINE/PubMed |
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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