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http://dx.doi.org/10.3745/JIPS.04.0008

A Maximum Entropy-Based Bio-Molecular Event Extraction Model that Considers Event Generation  

Lee, Hyoung-Gyu (Dept. of Computer Science, Korea University)
Park, So-Young (Dept. of Game Design & Development, SangMyung University)
Rim, Hae-Chang (Dept. of Computer Science, Korea University)
Lee, Do-Gil (Research Institute of Korean Studies, Korea University)
Chun, Hong-Woo (Technology Information Analysis Center, KISTI)
Publication Information
Journal of Information Processing Systems / v.11, no.2, 2015 , pp. 248-265 More about this Journal
Abstract
In this paper, we propose a maximum entropy-based model, which can mathematically explain the bio-molecular event extraction problem. The proposed model generates an event table, which can represent the relationship between an event trigger and its arguments. The complex sentences with distinctive event structures can be also represented by the event table. Previous approaches intuitively designed a pipeline system, which sequentially performs trigger detection and arguments recognition, and thus, did not clearly explain the relationship between identified triggers and arguments. On the other hand, the proposed model generates an event table that can represent triggers, their arguments, and their relationships. The desired events can be easily extracted from the event table. Experimental results show that the proposed model can cover 91.36% of events in the training dataset and that it can achieve a 50.44% recall in the test dataset by using the event table.
Keywords
Bioinformatics; Event Extraction; Maximum Entropy; Text-Mining;
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Times Cited By KSCI : 1  (Citation Analysis)
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