@inProceedings{
 title = {Coreference Based Event-Argument Relation Extraction on Biomedical Text},
 type = {inProceedings},
 year = {2010},
 pages = {1-15},
 websites = {http://cl.naist.jp/~katsumasa-y/publications/smbm10.pdf},
 id = {109bf1f7-b506-3dd7-bc9e-8cbe503bc763},
 created = {2011-12-29T19:53:53.000Z},
 file_attached = {false},
 profile_id = {5284e6aa-156c-3ce5-bc0e-b80cf09f3ef6},
 group_id = {066b42c8-f712-3fc3-abb2-225c158d2704},
 last_modified = {2017-03-14T14:36:19.698Z},
 tags = {coreference resolution,relation extraction},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Yoshikawa2010},
 private_publication = {false},
 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 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.},
 bibtype = {inProceedings},
 author = {Yoshikawa, Katsumasa and Riedel, Sebastian and Hirao, Tsutomu and Asahara, Masayuki and Matsumoto, Yuji},
 booktitle = {Proceedings of the Fourth Symposium on Semantic Mining in Biomedicine SMBM 2010}
}

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