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Bioinformatics Advance Access originally published online on December 9, 2008
Bioinformatics 2009 25(3):394-400; doi:10.1093/bioinformatics/btn631
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© 2008 The Author(s)
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.

Evaluating contributions of natural language parsers to protein–protein interaction extraction

Yusuke Miyao 1, Kenji Sagae 2, Rune Sætre 1, Takuya Matsuzaki 1 and Jun'ichi Tsujii 1,3,4

1Department of Computer Science, University of Tokyo, Tokyo, Japan, 2Institute for Creative Technologies, University of Southern California, CA, USA, 3School of Computer Science, University of Manchester and 4National Center for Text Mining, Manchester, UK

*To whom correspondence should be addressed.


  Abstract

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/downloads/downloads.cgi. The evaluated parsers are also available online from each developer's site.

Contact: yusuke{at}is.s.u-tokyo.ac.jp

Associate Editor: Jonathan Wren

Received on September 18, 2008; revised on November 9, 2008; accepted on December 3, 2008

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