JIPS (한국정보처리학회)
Current Result Document : 3 / 3
| 한글제목(Korean Title) |
Utilizing Various Natural Language Processing Techniques for Biomedical Interaction Extraction |
| 영문제목(English Title) |
Utilizing Various Natural Language Processing Techniques for Biomedical Interaction Extraction |
| 저자(Author) |
Kyung-Mi Park
Han-Cheol Cho
Hae-Chang Rim
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| 원문수록처(Citation) |
VOL 07 NO. 03 PP. 0459 ~ 0472 (2011. 09) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
The vast number of biomedical literature is an important source of biomedical interaction information discovery. However, it is complicated to obtain interaction information from them because most of them are not easily readable by machine. In this paper, we present a method for extracting biomedical interaction information assuming that the biomedical Named Entities (NEs) are already identified. The proposed method labels all possible pairs of given biomedical NEs as INTERACTION or NOINTERACTION by using a Maximum Entropy (ME) classifier. The features used for the classifier are obtained by applying various NLP techniques such as POS tagging, base phrase recognition, parsing and predicate-argument recognition. Especially, specific verb predicates (activate, inhibit, diminish and etc.) and their biomedical NE arguments are very useful features for identifying interactive NE pairs. Based on this, we devised a twostep method: 1) an interaction verb extraction step to find biomedically salient verbs, and 2) an argument relation identification step to generate partial predicate-argument structures between extracted interaction verbs and their NE arguments. In the experiments, we analyzed how much each applied NLP technique improves the performance. The proposed method can be completely improved by more than 2% compared to the baseline method. The use of external contextual features, which are obtained from outside of NEs, is crucial for the performance improvement. We also compare the performance of the proposed method against the co-occurrence-based and the rule-based methods. The result demonstrates that the proposed method considerably improves the performance.
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| 키워드(Keyword) |
Biomedical Interaction Extraction
Natural Language Processing
Interaction Verb Extraction
Argument Relation Identification
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| 파일첨부 |
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