JIPS (한국정보처리학회)
Current Result Document :
| 한글제목(Korean Title) |
A Maximum Entropy-Based Bio-Molecular Event Extraction Model that Considers Event Generation |
| 영문제목(English Title) |
A Maximum Entropy-Based Bio-Molecular Event Extraction Model that Considers Event Generation |
| 저자(Author) |
Hyoung-Gyu Lee
So-Young Park
Hae-Chang Rim
Do-Gil Lee
Hong-Woo Chun
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| 원문수록처(Citation) |
VOL 11 NO. 02 PP. 0248 ~ 0265 (2015. 06) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
In this paper, we propose a maximum entropy-based model, which can mathematically explain the biomolecular 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 vents 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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| 키워드(Keyword) |
Bioinformatics
Event Extraction
Maximum Entropy
Text-Mining
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| 파일첨부 |
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