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Australasian Joint Conference on Artificial Intelligence

AI 2017: AI 2017: Advances in Artificial Intelligence pp 237–249Cite as

Random Set to Interpret Topic Models in Terms of Ontology Concepts

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10400))

Abstract

Topic modelling is a popular technique in text mining. However, discovered topic models are difficult to interpret due to incoherence and lack of background context. Many applications require an accurate interpretation of topic models so that both users and machines can use them effectively. Taking the advantage of random set and a domain ontology, this research can interpret the topic models. The interpretation is evaluated by comparing it with different baseline models on two standard datasets. The results show that the performance of the interpretation is significantly better than baseline models.

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Notes

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Acknowledgment

This research was partially supported by Grant DP140103157 from the Australian Research Council (ARC Discovery Project).

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Authors and Affiliations

  1. Electrical Engineering and Computer Science School, Queensland University of Technology (QUT), Brisbane, 4001, Australia

    Md Abul Bashar & Yuefeng Li

Authors
  1. Md Abul Bashar
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  2. Yuefeng Li
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Corresponding author

Correspondence to Md Abul Bashar .

Editor information

Editors and Affiliations

  1. La Trobe University, Melbourne, Australia

    Wei Peng

  2. La Trobe Business School, La Trobe University, Bundoora, Victoria, Australia

    Damminda Alahakoon

  3. RMIT University, Melbourne, Australia

    Xiaodong Li

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Bashar, M.A., Li, Y. (2017). Random Set to Interpret Topic Models in Terms of Ontology Concepts. In: Peng, W., Alahakoon, D., Li, X. (eds) AI 2017: Advances in Artificial Intelligence. AI 2017. Lecture Notes in Computer Science(), vol 10400. Springer, Cham. https://doi.org/10.1007/978-3-319-63004-5_19

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  • DOI: https://doi.org/10.1007/978-3-319-63004-5_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63003-8

  • Online ISBN: 978-3-319-63004-5

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