Detecting entity relations as a supporting task for bio-molecular event extraction

Authors:
Sofie Van Landeghem;Thomas Abeel;Bernard De Baets;Yves Van de Peer
Affiliations:
VIB, Belgium and Ghent University, Belgium;VIB, Belgium and Ghent University, Belgium and Broad Institute of MIT and Harvard, Cambridge, MA;Ghent University, Belgium;VIB, Belgium and Ghent University, Belgium
Venue:
BioNLP Shared Task '11 Proceedings of the BioNLP Shared Task 2011 Workshop
Year:
2011

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Abstract

Recently, the focus in the BioNLP domain has shifted from binary relations to more expressive event representations, largely owing to the international popularity of the BioNLP Shared Task (ST) of 2009. This year, the ST'11 provides a further generalization on three key aspects: text type, subject domain, and targeted event types. One of the supporting tasks established to provide more fine-grained text predictions is the extraction of entity relations. We have implemented an extraction system for such non-causal relations between named entities and domain terms, applying semantic spaces and machine learning techniques. Our system ranks second of four participating teams, achieving 37.04% precision, 47.48% recall and 41.62% F-score.