@inProceedings{
 title = {Automatic construction of predicate-argument structure patterns for biomedical information extraction},
 type = {inProceedings},
 year = {2006},
 identifiers = {[object Object]},
 pages = {284},
 issue = {July},
 websites = {http://portal.acm.org/citation.cfm?doid=1610075.1610116},
 publisher = {Association for Computational Linguistics},
 city = {Morristown, NJ, USA},
 id = {14e88afc-431a-3e09-a2f5-edfd1dc4c050},
 created = {2012-05-10T14:51:03.000Z},
 accessed = {2012-04-02},
 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 = {predicate argument structures},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Yakushiji2006},
 private_publication = {false},
 abstract = {This paper presents a method of automatically constructing information extraction patterns on predicate-argument structures (PASs) obtained by full parsing from a smaller training corpus. Because PASs represent generalized structures for syntactical variants, patterns on PASs are expected to be more generalized than those on surface words. In addition, patterns are divided into components to improve recall and we introduce a Support Vector Machine to learn a prediction model using pattern matching results. In this paper, we present experimental results and analyze them on how well protein-protein interactions were extracted from MEDLINE abstracts. The results demonstrated that our method improved accuracy compared to a machine learning approach using surface word/part-of-speech patterns.},
 bibtype = {inProceedings},
 author = {Yakushiji, Akane and Miyao, Yusuke and Ohta, Tomoko and Tateisi, Yuka and Tsujii, Jun'ichi},
 booktitle = {Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing - EMNLP '06}
}

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