A Markov logic approach to bio-molecular event extraction

Authors:
Sebastian Riedel;Hong-Woo Chun;Toshihisa Takagi;Jun'ichi Tsujii
Affiliations:
Research Organization of Information and System, Japan and University of Tokyo, Japan;Research Organization of Information and System, Japan and University of Tokyo, Japan;Research Organization of Information and System, Japan and University of Tokyo, Japan;University of Tokyo, Japan and University of Manchester, UK and National Centre for Text Mining, UK
Venue:
BioNLP '09 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing: Shared Task
Year:
2009

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Abstract

In this paper we describe our entry to the BioNLP 2009 Shared Task regarding biomolecular event extraction. Our work can be described by three design decisions: (1) instead of building a pipeline using local classifier technology, we design and learn a joint probabilistic model over events in a sentence; (2) instead of developing specific inference and learning algorithms for our joint model, we apply Markov Logic, a general purpose Statistical Relation Learning language, for this task; (3) we represent events as relational structures over the tokens of a sentence, as opposed to structures that explicitly mention abstract event entities. Our results are competitive: we achieve the 4th best scores for task 1 (in close range to the 3rd place) and the best results for task 2 with a 13 percent point margin.