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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