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Bioinformatics Advance Access published online on July 15, 2004
Bioinformatics, doi:10.1093/bioinformatics/bth409
Bioinformatics © Oxford University Press 2004; all rights reserved
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Received April 19, 2004
Revised July 4, 2004
Accepted July 5, 2004

Article

Extracting gene pathway relations using a hybrid grammar: the Arizona relation parser

D. M. McDonald 1*, H. Chen 1, H. Su 1, B. B. Marshall 1

1 Artificial Intelligence Lab, MIS Department, University of Arizona, 1130 E. Helen St., Tucson, AZ 85721, USA

* To whom correspondence should be addressed. E-mail: dmm{at}eller.arizona.edu.


  Abstract

Motivation: Text mining research in the biomedical domain has been motivated by the rapid growth of new research findings. Improving the accessibility of findings has potential to speed hypothesis generation.

Results: We present the Arizona Relation Parser that differs from other parsers in its use of a broad coverage syntax-semantic hybrid grammar. While syntax grammars have generally been tested over more documents, semantic grammars have outperformed them in precision and recall. We combined access to syntax and semantic information from a single grammar. The parser was trained using 40 PubMed abstracts and then tested using 100 unseen abstracts, half for precision and half for recall. Expert evaluation showed that the parser extracted biologically relevant relations with 89 percent precision. Recall of expert identified relations with semantic filtering was 35 percent and 61 percent before semantic filtering. Such results approach the higher-performing semantic parsers. However, the AZ parser was tested over a greater variety of writing styles and semantic content.

Availability: Relations extracted from over 600,000 PubMed abstracts are available for retrieval and visualization at http://econport.arizona.edu:8080/NetVis/index.html.


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[Abstract] [Full Text] [PDF]


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