Please use this identifier to cite or link to this item:
Title: A novel topic model for automatic term extraction
Authors: Li, S
Li, J
Song, T
Li, W 
Chang B
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
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.
Keywords: Term extraction
Topic model
ISBN: 978-1-4503-2034-4
DOI: 10.1145/2484028.2484106
Appears in Collections:Conference Paper

View full-text via PolyU eLinks SFX Query
Show full item record


Last Week
Last month
Citations as of Aug 19, 2020

Page view(s)

Last Week
Last month
Citations as of Sep 20, 2020

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.