Abstract
This paper presents a new approach that exploits coreference information to extract event-argument (E-A) relations from biomedical documents. This approach has two advantages: (1) it can extract a large number of valuable E-A relations for document understanding based on the concept of salience in discourse ; (2) it enables us to identify cross-sentence E-A using transitivity involving coreference relations. We propose two coreference-based models: a pipeline based on an Support Vector Machine (SVM) classifier, and a joint Markov Logic Network (MLN). We show the effectiveness of these models on GENIA Event Corpus.
