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	A novel topic model for automatic term extraction
	Li, Sujian ; Li, Jiwei ; Song, Tao ; Li, Wenjie ; Chang, Baobao
	2013
英文摘要	Automatic term extraction (ATE) aims at extracting domain-specific terms from a corpus of a certain domain. Termhood is one essential measure for judging whether a phrase is a term. Previous researches on termhood mainly depend on the word frequency information. In this paper, we propose to compute termhood based on semantic representation of words. A novel topic model, namely i-SWB, is developed to map the domain corpus into a latent semantic space, which is composed of some general topics, a background topic and a documents-specific topic. Experiments on four domains demonstrate that our approach outperforms the state-of-the-art ATE approaches. Copyright ? 2013 ACM.; EI; 0
语种	英语
DOI标识	10.1145/2484028.2484106
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/411746]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Li, Sujian,Li, Jiwei,Song, Tao,et al. A novel topic model for automatic term extraction. 2013-01-01.

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