@article {bioinflmu-254,
	title = {{RelEx - Relation extraction using dependency parse trees}},
	journal = {Bioinformatics},
	volume = {23},
	number = {3},
	year = {2007},
	pages = {365-371},
	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/).},
	keywords = {networks, textmining},
	doi = {10.1093/bioinformatics/btl616},
	author = {Katrin Fundel and K{\"u}ffner, R and Ralf Zimmer}
}