Coreference Based Event-Argument Relation Extraction on Biomedical Text. Yoshikawa, K., Riedel, S., Hirao, T., Asahara, M., & Matsumoto, Y. In Proceedings of the Fourth Symposium on Semantic Mining in Biomedicine SMBM 2010, pages 1-15, 2010.
Coreference Based Event-Argument Relation Extraction on Biomedical Text [pdf]Website  abstract   bibtex   
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 based on the concept of salience in discourse (Grosz et al., 1995) ; (2) it enables us to iden- tify E-A relations over sentence bound- aries (cross-links) using transitivity involv- ing coreference relations. We propose two coreference-based models: a pipeline based on Support Vector Machine (SVM) classifiers, and a joint Markov Logic Net- work (MLN).We show the effectiveness of these models on a biomedical event corpus. The both models outperform the systems without coreference information. When compared with the two models, joint MLN outperforms pipeline SVM with gold coref- erence information.

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