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Enhanced Topic Representation by Ambiguity Handling

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Web Information Systems Engineering – WISE 2022 (WISE 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13724))

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Abstract

Most of the existing semantic-based topic models and topic generation approaches use external knowledgebases or ontology to interpret the meanings of the words. However, general ontologies do not cover many ambiguous or specific domain-related words in a text collection. Hence those ambiguous or domain-specific words are neglected in capturing the meanings in topic generation. In this paper, we introduce an approach to disambiguate the unmatched words in a text collection based on related and similar meaning words. Word embeddings are applied to discover similar or related words. We evaluated the topic generation approach with our ambiguity handling technique with a set of state-of-the-art systems which uses an external ontology. Our approach outperformed, and the generated topics were more meaningful. Our ambiguity handling approach interpreted all the important words and included them in the topic generation process.

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Authors and Affiliations

  1. School of Computer Science, Queensland University of Technology, Brisbane, Australia

    Dakshi Kapugama Geeganage, Yue Xu, Darshika Koggalahewa & Yuefeng Li

Authors
  1. Dakshi Kapugama Geeganage
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  2. Yue Xu
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  3. Darshika Koggalahewa
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  4. Yuefeng Li
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Corresponding author

Correspondence to Dakshi Kapugama Geeganage .

Editor information

Editors and Affiliations

  1. University of Pau and Pays de l'Adour, Anglet, France

    Richard Chbeir

  2. The University of Queensland, Brisbane, QLD, Australia

    Helen Huang

  3. Sapienza Università di Roma, Rome, Italy

    Fabrizio Silvestri

  4. Open University of Cyprus, Nicosia, Cyprus

    Yannis Manolopoulos

  5. The New Cyber Research Department, Peng Cheng Laboratory, Shenzhen, China

    Yanchun Zhang

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Geeganage, D.K., Xu, Y., Koggalahewa, D., Li, Y. (2022). Enhanced Topic Representation by Ambiguity Handling. In: Chbeir, R., Huang, H., Silvestri, F., Manolopoulos, Y., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2022. WISE 2022. Lecture Notes in Computer Science, vol 13724. Springer, Cham. https://doi.org/10.1007/978-3-031-20891-1_25

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  • DOI: https://doi.org/10.1007/978-3-031-20891-1_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20890-4

  • Online ISBN: 978-3-031-20891-1

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