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Bioinformatics Advance Access originally published online on December 1, 2006
Bioinformatics 2007 23(3):365-371; doi:10.1093/bioinformatics/btl616
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© The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

RelEx—Relation extraction using dependency parse trees

Katrin Fundel *, Robert Küffner and Ralf Zimmer

Institut für Informatik, Ludwig-Maximilians-Universität München Amalienstrasse 17, 80333 München, Germany

*To whom correspondence should be addressed.


  Abstract

Motivation: The discovery of regulatory pathways, signal cascades, metabolic processes or disease models requires knowledge on individual relations like e.g. physical or regulatory interactions between genes and proteins. Most interactions mentioned in the free text of biomedical publications are not yet contained in structured databases.

Results: We developed RelEx, an approach for relation extraction from free text. It is based on natural language preprocessing producing dependency parse trees and applying a small number of simple rules to these trees. We applied RelEx on a comprehensive set of one million MEDLINE abstracts dealing with gene and protein relations and extracted ~150 000 relations with an estimated perfomance of both 80% precision and 80% recall.

Availability: The used natural language preprocessing tools are free for use for academic research. Test sets and relation term lists are available from our website (http://www.bio.ifi.lmu.de/publications/RelEx/).

Contact: katrin.fundel{at}bio.ifi.lmu.de

Associate Editor: Satoru Miyano

Received on August 31, 2006; revised on November 8, 2006; accepted on November 28, 2006

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