2007 RelExRelExtrUsingDepParseTrees

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Subject Headings: Relation Recognition from Text Algorithm, Protein-Protein Interaction, RelEx System.


  • Gene and protein names are identified by ProMiner System. (Hanisch et al., 2005)
    • Ranked #1 for two of three NER tests in BioCreative
  • It is based on matching to a synonym dictionary (Fundel and Zimmer, 2006)

Cited By



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/).


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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2007 RelExRelExtrUsingDepParseTreesKatrin Fundel
Robert Kuffner
Ralf Zimmer
RelEx - Relation Extraction Using Dependency Parse TreesThe Bioinformatics Journalhttp://www.biomedcentral.com/content/pdf/1471-2105-6-S1-S14.pdf2007