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Evaluating contributions of natural language parsers to protein–protein interaction extraction
Motivation: While text mining technologies for biomedical research have gained popularity as a way to take advantage of the explosive growth of information in text form in biomedical papers, selecting appropriate natural language processing (NLP) tools is still difficult for researchers who are not...
Autores principales: | , , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2009
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2639072/ https://www.ncbi.nlm.nih.gov/pubmed/19073593 http://dx.doi.org/10.1093/bioinformatics/btn631 |
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author | Miyao, Yusuke Sagae, Kenji Sætre, Rune Matsuzaki, Takuya Tsujii, Jun'ichi |
author_facet | Miyao, Yusuke Sagae, Kenji Sætre, Rune Matsuzaki, Takuya Tsujii, Jun'ichi |
author_sort | Miyao, Yusuke |
collection | PubMed |
description | Motivation: While text mining technologies for biomedical research have gained popularity as a way to take advantage of the explosive growth of information in text form in biomedical papers, selecting appropriate natural language processing (NLP) tools is still difficult for researchers who are not familiar with recent advances in NLP. This article provides a comparative evaluation of several state-of-the-art natural language parsers, focusing on the task of extracting protein–protein interaction (PPI) from biomedical papers. We measure how each parser, and its output representation, contributes to accuracy improvement when the parser is used as a component in a PPI system. Results: All the parsers attained improvements in accuracy of PPI extraction. The levels of accuracy obtained with these different parsers vary slightly, while differences in parsing speed are larger. The best accuracy in this work was obtained when we combined Miyao and Tsujii's Enju parser and Charniak and Johnson's reranking parser, and the accuracy is better than the state-of-the-art results on the same data. Availability: The PPI extraction system used in this work (AkanePPI) is available online at http://www-tsujii.is.s.u-tokyo.ac.jp/-100downloads/downloads.cgi. The evaluated parsers are also available online from each developer's site. Contact: yusuke@is.s.u-tokyo.ac.jp |
format | Text |
id | pubmed-2639072 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2009 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-26390722009-02-25 Evaluating contributions of natural language parsers to protein–protein interaction extraction Miyao, Yusuke Sagae, Kenji Sætre, Rune Matsuzaki, Takuya Tsujii, Jun'ichi Bioinformatics Original Papers Motivation: While text mining technologies for biomedical research have gained popularity as a way to take advantage of the explosive growth of information in text form in biomedical papers, selecting appropriate natural language processing (NLP) tools is still difficult for researchers who are not familiar with recent advances in NLP. This article provides a comparative evaluation of several state-of-the-art natural language parsers, focusing on the task of extracting protein–protein interaction (PPI) from biomedical papers. We measure how each parser, and its output representation, contributes to accuracy improvement when the parser is used as a component in a PPI system. Results: All the parsers attained improvements in accuracy of PPI extraction. The levels of accuracy obtained with these different parsers vary slightly, while differences in parsing speed are larger. The best accuracy in this work was obtained when we combined Miyao and Tsujii's Enju parser and Charniak and Johnson's reranking parser, and the accuracy is better than the state-of-the-art results on the same data. Availability: The PPI extraction system used in this work (AkanePPI) is available online at http://www-tsujii.is.s.u-tokyo.ac.jp/-100downloads/downloads.cgi. The evaluated parsers are also available online from each developer's site. Contact: yusuke@is.s.u-tokyo.ac.jp Oxford University Press 2009-02-01 2008-12-09 /pmc/articles/PMC2639072/ /pubmed/19073593 http://dx.doi.org/10.1093/bioinformatics/btn631 Text en © 2008 The Author(s) http://creativecommons.org/licenses/by-nc/2.0/uk/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Papers Miyao, Yusuke Sagae, Kenji Sætre, Rune Matsuzaki, Takuya Tsujii, Jun'ichi Evaluating contributions of natural language parsers to protein–protein interaction extraction |
title | Evaluating contributions of natural language parsers to protein–protein interaction extraction |
title_full | Evaluating contributions of natural language parsers to protein–protein interaction extraction |
title_fullStr | Evaluating contributions of natural language parsers to protein–protein interaction extraction |
title_full_unstemmed | Evaluating contributions of natural language parsers to protein–protein interaction extraction |
title_short | Evaluating contributions of natural language parsers to protein–protein interaction extraction |
title_sort | evaluating contributions of natural language parsers to protein–protein interaction extraction |
topic | Original Papers |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2639072/ https://www.ncbi.nlm.nih.gov/pubmed/19073593 http://dx.doi.org/10.1093/bioinformatics/btn631 |
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