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공공누리This item is licensed Korea Open Government License

dc.contributor.author
송사광
dc.contributor.author
최윤수
dc.contributor.author
성원경
dc.contributor.author
전홍우
dc.contributor.author
정창후
dc.contributor.author
최성필
dc.date.accessioned
2019-08-28T07:41:23Z
dc.date.available
2019-08-28T07:41:23Z
dc.date.issued
2013-01-01
dc.identifier.issn
1343-4500
dc.identifier.uri
https://repository.kisti.re.kr/handle/10580/14192
dc.description.abstract
The multi-word terminology is recognized based on statistical information obtained from large amount of documents in general. However, this kind of approaches is dependent on the domain information, so that it is very limited to use when it is hard to apply to a new domain or extract statistical information in the domain. Hence, we have developed a multi-word terminology recognition system independent with domain, which is more effective than the local statistical information-based methodology. It utilizes dictionaries, syntactic features, and web search results after excluding the statistical information extracted from the target literature records. We achieved F-score 80.9 and 6.4% improvement after comparing the proposed approach with the related approach, C-value, which has been widely used and is based on local domain frequencies.
dc.language
eng
dc.relation.ispartofseries
Information : an international interdisciplinary journal
dc.title
Domain Independent Recognition of Multi-word Technology Using Web Search
dc.subject.keyword
Terminology Recognition
dc.subject.keyword
Text Mining
dc.subject.keyword
Machine Learning
dc.subject.keyword
Information Extraction
Appears in Collections:
7. KISTI 연구성과 > 학술지 발표논문
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