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논문 기본 정보

자료유형
학술저널
저자정보
Yaoyi Xi (Zhengzhou Information Science and Technology Institute) Bicheng Li (Zhengzhou Information Science and Technology Institute) Yang Liu (Zhengzhou Information Science and Technology Institute)
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.

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Abstract
Ⅰ. INTRODUCTION
Ⅱ. PROBLEM DEFINITION AND NOTATION
Ⅲ. RELATED WORK
Ⅳ. METHODOLOGY
Ⅴ. EXPERIMENTS AND ANALYSIS
Ⅵ. CONCLUSION AND FUTURE WORK
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UCI(KEPA) : I410-ECN-0101-2016-569-001673815