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dc.contributor.authorFranciscus, N
dc.contributor.authorRen, X
dc.contributor.authorWang, J
dc.contributor.authorStantic, B
dc.contributor.editorNguyen, NT
dc.contributor.editorGaol, FL
dc.contributor.editorHong, TP
dc.contributor.editorTrawinski, B
dc.date.accessioned2020-03-26T00:29:19Z
dc.date.available2020-03-26T00:29:19Z
dc.date.issued2019
dc.identifier.isbn9783030147983
dc.identifier.issn0302-9743
dc.identifier.doi10.1007/978-3-030-14799-0_11
dc.identifier.urihttp://hdl.handle.net/10072/392638
dc.description.abstractIn the era of information overload, text clustering plays an important part in the analysis processing pipeline. Partitioning high-quality texts into unseen categories tremendously helps applications in information retrieval, databases, and business intelligence domains. Short texts from social media environment such as tweets, however, remain difficult to interpret due to the broad aspects of contexts. Traditional text similarity approaches only rely on the lexical matching while ignoring the semantic meaning of words. Recent advances in distributional semantic space have opened an alternative approach in utilizing high-quality word embeddings to aid the interpretation of text semantics. In this paper, we investigate the word mover’s distance metrics to automatically cluster short text using the word semantic information. We utilize the agglomerative strategy as the clustering method to efficiently group texts based on their similarity. The experiment indicates the word mover’s distance outperformed other standard metrics in the short text clustering task.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherSpringer
dc.relation.ispartofconferencename11th Asian Conference on Intelligent Information and Database Systems (ACIIDS 2019)
dc.relation.ispartofconferencetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.relation.ispartofdatefrom2019-04-08
dc.relation.ispartofdateto2019-04-11
dc.relation.ispartoflocationYogyakarta, Indonesia
dc.relation.ispartofpagefrom128
dc.relation.ispartofpagefrom12 pages
dc.relation.ispartofpageto139
dc.relation.ispartofpageto12 pages
dc.relation.ispartofseriesLecture Notes in Computer Science
dc.relation.ispartofvolume11431
dc.subject.fieldofresearchArtificial Intelligence and Image Processing
dc.subject.fieldofresearchcode0801
dc.subject.keywordsScience & Technology
dc.subject.keywordsComputer Science, Artificial Intelligence
dc.subject.keywordsComputer Science, Information Systems
dc.subject.keywordsComputer Science, Theory & Methods
dc.titleWord Mover’s Distance for Agglomerative Short Text Clustering
dc.typeConference output
dc.type.descriptionE1 - Conferences
dcterms.bibliographicCitationFranciscus, N; Ren, X; Wang, J; Stantic, B, Word Mover’s Distance for Agglomerative Short Text Clustering, Intelligent Information and Database Systems , 2019, 11431, pp. 128-139
dc.date.updated2020-03-26T00:15:33Z
gro.hasfulltextNo Full Text
gro.griffith.authorWang, John
gro.griffith.authorStantic, Bela
gro.griffith.authorFranciscus, Nigel
gro.griffith.authorRen, Xuguang


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