Automatic extraction of conceptual labels from topic models

Muşat, C; Trǎuşan-Matu, S; Velcin, J; Rizoiu, MA

Automatic extraction of conceptual labels from topic models

Muşat, C Trǎuşan-Matu, S Velcin, J Rizoiu, MA

Permalink

Publication Type:: Journal Article
Citation:: UPB Scientific Bulletin, Series C: Electrical Engineering, 2012, 74, (2), pp. 57-68
Issue Date:: 2012-01-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download Accepted versionAdobe PDF (225.88 kB)

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Muşat, C
dc.contributor.author	Trǎuşan-Matu, S
dc.contributor.author	Velcin, J
dc.contributor.author	Rizoiu, MA
dc.date.accessioned	2021-01-22T06:40:00Z
dc.date.available	2021-01-22T06:40:00Z
dc.date.issued	2012-01-01
dc.identifier.citation	UPB Scientific Bulletin, Series C: Electrical Engineering, 2012, 74, (2), pp. 57-68
dc.identifier.issn	2286-3540
dc.identifier.issn	2286-3559
dc.identifier.uri	http://hdl.handle.net/10453/145563
dc.description.abstract	This work outlines a novel system that automatically extracts conceptual labels for statistically obtained topics. By creating a projection of the topic, which is a distribution over all the vocabulary words, over the WordNet ontology we succeed in associating concepts to the said groups of words. The most important contributions of this paper are connected to the validation of the role of these concepts as topical labels and the determination of correlations that emerge between the utility of these labels and the strength of the relation between the concepts and the topics.
dc.language	en
dc.relation.ispartof	UPB Scientific Bulletin, Series C: Electrical Engineering
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Automatic extraction of conceptual labels from topic models
dc.type	Journal Article
utslib.citation.volume	74
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	2021-01-22T06:39:37Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	74
utslib.citation.issue	2

Abstract:

This work outlines a novel system that automatically extracts conceptual labels for statistically obtained topics. By creating a projection of the topic, which is a distribution over all the vocabulary words, over the WordNet ontology we succeed in associating concepts to the said groups of words. The most important contributions of this paper are connected to the validation of the role of these concepts as topical labels and the determination of correlations that emerge between the utility of these labels and the strength of the relation between the concepts and the topics.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/145563