Concept-based topic model improvement

Musat, C; Velcin, J; Rizoiu, MA; Trausan-Matu, S

Concept-based topic model improvement

Musat, C Velcin, J Rizoiu, MA Trausan-Matu, S

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Publisher:: Springer Berlin Heidelberg
Publication Type:: Conference Proceeding
Citation:: Studies in Computational Intelligence, 2011, 369, pp. 133-142
Issue Date:: 2011-10-24

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Field	Value	Language
dc.contributor.author	Musat, C
dc.contributor.author	Velcin, J
dc.contributor.author	Rizoiu, MA
dc.contributor.author	Trausan-Matu, S
dc.contributor.editor	Ryzko, D
dc.contributor.editor	Rybinski, H
dc.contributor.editor	Gawrysiak, P
dc.contributor.editor	Kryszkiewicz, M
dc.date.accessioned	2021-01-22T06:38:39Z
dc.date.available	2021-01-22T06:38:39Z
dc.date.issued	2011-10-24
dc.identifier.citation	Studies in Computational Intelligence, 2011, 369, pp. 133-142
dc.identifier.isbn	9783642227318
dc.identifier.issn	1860-949X
dc.identifier.issn	1860-9503
dc.identifier.uri	http://hdl.handle.net/10453/145562
dc.description.abstract	We propose a system which employs conceptual knowledge to improve topic models by removing unrelated words from the simplified topic description. We use WordNet to detect which topical words are not conceptually similar to the others and then test our assumptions against human judgment. Results obtained on two different corpora in different test conditions show that the words detected as unrelated had a much greater probability than the others to be chosen by human evaluators as not being part of the topic at all. We prove that there is a strong correlation between the said probability and an automatically calculated topical fitness and we discuss the variation of the correlation depending on the method and data used. © 2011 Springer-Verlag Berlin Heidelberg.
dc.language	en
dc.publisher	Springer Berlin Heidelberg
dc.relation.ispartof	Studies in Computational Intelligence
dc.relation.ispartofseries	Studies in Computational Intelligence
dc.relation.isbasedon	10.1007/978-3-642-22732-5_12
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	This is a post-peer-review, pre-copyedit version of an article published in Studies in Computational Intelligence. The final authenticated version is available online at: https://dx.doi.org/10.1007/978-3-642-22732-5_12.	en_US
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Concept-based topic model improvement
dc.type	Conference Proceeding
utslib.citation.volume	369
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/A/DRsch The Data Science Institute
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	open_access	*
dc.date.updated	2021-01-22T06:38:15Z
pubs.publication-status	Published
pubs.volume	369

Abstract:

We propose a system which employs conceptual knowledge to improve topic models by removing unrelated words from the simplified topic description. We use WordNet to detect which topical words are not conceptually similar to the others and then test our assumptions against human judgment. Results obtained on two different corpora in different test conditions show that the words detected as unrelated had a much greater probability than the others to be chosen by human evaluators as not being part of the topic at all. We prove that there is a strong correlation between the said probability and an automatically calculated topical fitness and we discuss the variation of the correlation depending on the method and data used. © 2011 Springer-Verlag Berlin Heidelberg.

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http://hdl.handle.net/10453/145562