title: RelEx--relation extraction using dependency parse trees.
abstract: BACKGROUND
  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.
  
  
  BACKGROUND
  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/).
authors.affiliation: Institut für Informatik, Ludwig-Maximilians-Universität München, Amalienstrasse 17, 80333 München, Germany. katrin.fundel@bio.ifi.lmu.de
authors.forename: Katrin
authors.initials: K
authors.lastname: Fundel
authors.forename: Robert
authors.initials: R
authors.lastname: Küffner
authors.forename: Ralf
authors.initials: R
authors.lastname: Zimmer
mesh_terms: D000465:Algorithms; D003628:Database Management Systems; D015870:Gene Expression / Q000502:physiology*; D016247:Information Storage and Retrieval / Q000379:methods*; D016239:MEDLINE; D009323:Natural Language Processing; D025941:Protein Interaction Mapping / Q000379:methods*; D011506:Proteins / Q000145:classification / Q000235:genetics* / Q000378:metabolism*; D012984:Software
chemical_list: D011506:Proteins