Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/39876
DC FieldValueLanguage
dc.contributorDepartment of Computing-
dc.creatorLi, S-
dc.creatorLi, J-
dc.creatorSong, T-
dc.creatorLi, W-
dc.creatorChang B-
dc.date.accessioned2016-05-11T10:18:19Z-
dc.date.available2016-05-11T10:18:19Z-
dc.identifier.isbn978-1-4503-2034-4-
dc.identifier.urihttp://hdl.handle.net/10397/39876-
dc.language.isoenen_US
dc.subjectTerm extractionen_US
dc.subjectTermhooden_US
dc.subjectTopic modelen_US
dc.titleA novel topic model for automatic term extractionen_US
dc.typeConference Paperen_US
dc.identifier.spage885-
dc.identifier.epage888-
dc.identifier.doi10.1145/2484028.2484106-
dcterms.abstractAutomatic 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.-
dcterms.bibliographicCitationSIGIR '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-
dcterms.issued2013-
dc.relation.conferenceInternational ACM SIGIR Conference on Research and Development in Information Retrieval [SIGIR]-
dc.identifier.rosgroupidr72549-
dc.description.ros2013-2014 > Academic research: refereed > Refereed conference paper-
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