A Markov Logic Approach to Bio-Molecular Event Extraction

Abstract

In this paper we describe our entry to the BioNLP 2009 Shared Task regarding bio- molecular 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 spe- cific inference and learning algorithms for our joint model, we apply Markov Logic, a general purpose Statistical Relation Learn- ing language, for this task; (3) we represent events as relational structures over the to- kens of a sentence, as opposed to structures that explicitly mention abstract event en- tities. 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.