Please use this identifier to cite or link to this item: https://doi.org/10.1145/1645953.1646071
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dc.titleExploiting internal and external semantics for the clustering of short texts using world knowledge
dc.contributor.authorHu, X.
dc.contributor.authorSun, N.
dc.contributor.authorZhang, C.
dc.contributor.authorChua, T.-S.
dc.date.accessioned2013-07-04T08:44:13Z
dc.date.available2013-07-04T08:44:13Z
dc.date.issued2009
dc.identifier.citationHu, X.,Sun, N.,Zhang, C.,Chua, T.-S. (2009). Exploiting internal and external semantics for the clustering of short texts using world knowledge. International Conference on Information and Knowledge Management, Proceedings : 919-928. ScholarBank@NUS Repository. <a href="https://doi.org/10.1145/1645953.1646071" target="_blank">https://doi.org/10.1145/1645953.1646071</a>
dc.identifier.isbn9781605585123
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/42135
dc.description.abstractClustering of short texts, such as snippets, presents great challenges in existing aggregated search techniques due to the problem of data sparseness and the complex semantics of natural language. As short texts do not provide sufficient term occurring information, traditional text representation methods, such as ''bag of words" model, have several limitations when directly applied to short texts tasks. In this paper, we propose a novel framework to improve the performance of short texts clustering by exploiting the internal semantics from original text and external concepts from world knowledge. The proposed method employs a hierarchical three-level structure to tackle the data sparsity problem of original short texts and reconstruct the corresponding feature space with the integration of multiple semantic knowledge bases - Wikipedia and WordNet. Empirical evaluation with Reuters and real web dataset demonstrates that our approach is able to achieve significant improvement as compared to the state-of-the-art methods. Copyright 2009 ACM.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1145/1645953.1646071
dc.sourceScopus
dc.subjectClustering
dc.subjectSemantic knowledge bases
dc.subjectShort texts
dc.subjectSyntactic structure
dc.typeConference Paper
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.doi10.1145/1645953.1646071
dc.description.sourcetitleInternational Conference on Information and Knowledge Management, Proceedings
dc.description.page919-928
dc.identifier.isiutNOT_IN_WOS
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