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

Hyoung-Gyu Lee, So-Young Park, Hae-Chang Rim, Do-Gil Lee and Hong-Woo Chun
Volume: 11, No: 2, Page: 248 ~ 265, Year: 2015
10.3745/JIPS.04.0008
Keywords: Bioinformatics, Event Extraction, Maximum Entropy, Text-Mining
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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.

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Cite this article
IEEE Style
Hyoung-Gyu Lee, So-Young Park, Hae-Chang Rim, Do-Gil Lee, and Hong-Woo Chun, "A Maximum Entropy-Based Bio-Molecular Event Extraction Model that Considers Event Generation," Journal of Information Processing Systems, vol. 11, no. 2, pp. 248~265, 2015. DOI: 10.3745/JIPS.04.0008.

ACM Style
Hyoung-Gyu Lee, So-Young Park, Hae-Chang Rim, Do-Gil Lee, and Hong-Woo Chun, "A Maximum Entropy-Based Bio-Molecular Event Extraction Model that Considers Event Generation," Journal of Information Processing Systems, 11, 2, (2015), 248~265. DOI: 10.3745/JIPS.04.0008.