RelEx--relation Extraction Using Dependency Parse Trees

Overview

Journal Bioinformatics

Publisher Oxford University Press

Specialty Biology

Date 2006 Dec 5

PMID 17142812

Citations 123

Authors

Katrin Fundel

Robert Kuffner

Ralf Zimmer

Affiliations

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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 approximately 150,000 relations with an estimated performance 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/).

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