This article is part of the supplement: Proceedings of the Fourth International Symposium on Semantic Mining in Biomedicine (SMBM)
Research
Coreference based event-argument relation extraction on biomedical text
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Corresponding authors: Katsumasa Yoshikawa katsumasa-y@is.naist.jp - Sebastian Riedel sebastian.riedel@gmail.com - Tsutomu Hirao hirao@cslab.kecl.ntt.co.jp - Masayuki Asahara masayu-a@is.naist.jp - Yuji Matsumoto matsu@is.naist.jp
1 Graduate School of Information Science, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara, Japan
2 University of Massachusetts, Amherst, Amherst, MA 01002, U.S
3 NTT Communication Science Laboratories, 2-4, Hikaridai, Seika-cho, Keihanna Science City, Kyoto, Japan
Journal of Biomedical Semantics 2011, 2(Suppl 5):S6 doi:10.1186/2041-1480-2-S5-S6
Published: 6 October 2011Abstract
This paper presents a new approach to exploit coreference information for extracting 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; (2) it enables us to identify E-A relations over sentence boundaries (cross-links) using transitivity of coreference relations. We propose two coreference-based models: a pipeline based on Support Vector Machine (SVM) classifiers, and a joint Markov Logic Network (MLN). We show the effectiveness of these models on a biomedical event corpus. Both models outperform the systems that do not use coreference information. When the two proposed models are compared to each other, joint MLN outperforms pipeline SVM with gold coreference information.