지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- Korean Institute of Information Scientists and Engineers Journal of Computing Science and Engineering Journal of Computing Science and Engineering Vol.9 No.2
- 발행연도
- 2015.6
- 수록면
- 73 - 82 (10page)
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초록· 키워드
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Understanding how the topic evolves is an important and challenging task. A topic usually consists of multiple related events, and the accurate identification of event evolution relationship plays an important role in topic evolution analysis. Existing research has used the traditional vector space model to represent the event, which cannot be used to accurately compute the semantic similarity between events. This has led to poor performance in identifying event evolution relationship. This paper suggests constructing a semantic aspect-based vector space model to represent the event: First, use hierarchical Dirichlet process to mine the semantic aspects. Then, construct a semantic aspect-based vector space model according to these aspects. Finally, represent each event as a point and measure the semantic relatedness between events in the space. According to our evaluation experiments, the performance of our proposed technique is promising and significantly outperforms the baseline methods.
목차
Abstract
Ⅰ. INTRODUCTION
Ⅱ. PROBLEM DEFINITION AND NOTATION
Ⅲ. RELATED WORK
Ⅳ. METHODOLOGY
Ⅴ. EXPERIMENTS AND ANALYSIS
Ⅵ. CONCLUSION AND FUTURE WORK
REFERENCES
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UCI(KEPA) : I410-ECN-0101-2016-569-001673815