Skip Navigation



Bioinformatics Advance Access published online on December 9, 2008
Bioinformatics, doi:10.1093/bioinformatics/btn631
This Article
Right arrow Advance Access manuscript (PDF) Freely available
Right arrowOA All Versions of this Article:
25/3/394    most recent
btn631v1
Right arrow Comments: Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when Comments are posted
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Google Scholar
Right arrow Articles by Miyao, Y.
Right arrow Articles by Tsujii, J.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Miyao, Y.
Right arrow Articles by Tsujii, J.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© 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, Japan
2Institute for Creative Technologies, University of Southern California, USA
3School of Computer Science, University of Manchester, UK
4National Center for Text Mining, UK

*To whom correspondence should be addressed. Dr. Yusuke Miyao, E-mail: yusuke{at}is.s.u-tokyo.ac.jp


  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 dif.cult for researchers who are not familiar with recent advances in NLP. This paper provides a comparative evaluation of several state-of-the-art natural language parsers, focusing on the task of extracting proteinprotein 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 (27)'s Enju parser and (6)'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: Dr. Jonathan Wren

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

Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?




Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.