Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/39876
Title: A novel topic model for automatic term extraction
Authors: Li, S
Li, J
Song, T
Li, W 
Chang B
Keywords: Term extraction
Termhood
Topic model
Issue Date: 2013
Source: SIGIR '13 Proceedings of the 36th international ACM SIGIR Conference on Research and Development in Information Retrieval, Dublin, Ireland, July 28 - August 1, 2013, p. 885-888 How to cite?
Abstract: 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.
URI: http://hdl.handle.net/10397/39876
ISBN: 978-1-4503-2034-4
DOI: 10.1145/2484028.2484106
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