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공공누리This item is licensed Korea Open Government License

dc.contributor.author
전홍우
dc.contributor.author
정창후
dc.contributor.author
성원경
dc.contributor.author
송사광
dc.contributor.author
최성필
dc.contributor.author
최윤수
dc.date.accessioned
2019-08-28T07:41:15Z
dc.date.available
2019-08-28T07:41:15Z
dc.date.issued
2013-01-10
dc.identifier.issn
1343-4500
dc.identifier.uri
https://repository.kisti.re.kr/handle/10580/14114
dc.description.abstract
Lots of valuable textual information is used to extract relations between named entities from literature. The composite kernel approach is proposed in this paper. The composite kernel approach calculates similarities based on the following information: (1) Phrase structure in convolution parse tree kernel that has shown encouraging results. (2) Predicateargument structure patterns. In other words, the approach deals with syntactic structure as well as semantic structure using a reciprocal method. The proposed approach was evaluated using various types of test collections, and it showed the better performance compared with those of previous approach, using only information from syntactic structures. In addition, it showed a better performance than those of the state of the art approach.
dc.language
eng
dc.relation.ispartofseries
Information : an international interdisciplinary journal
dc.title
Composite Kernel-based Relation Extraction using Predicate-Argument Structure
dc.subject.keyword
Composite Kernel
dc.subject.keyword
Relation extraction
dc.subject.keyword
Tree Kernel
dc.subject.keyword
Predicate-Argument structure pattern
Appears in Collections:
7. KISTI 연구성과 > 학술지 발표논문
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