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Combining concept hierarchies and statistical topic models

Published: 26 October 2008 Publication History

Abstract

Statistical topic models provide a general data-driven framework for automated discovery of high-level knowledge from large collections of text documents. While topic models can potentially discover a broad range of themes in a data set, the interpretability of the learned topics is not always ideal. Human-defined concepts, on the other hand, tend to be semantically richer due to careful selection of words to define concepts but they tend not to cover the themes in a data set exhaustively. In this paper, we propose a probabilistic framework to combine a hierarchy of human-defined semantic concepts with statistical topic models to seek the best of both worlds. Experimental results using two different sources of concept hierarchies and two collections of text documents indicate that this combination leads to systematic improvements in the quality of the associated language models as well as enabling new techniques for inferring and visualizing the semantics of a document.

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Cited By

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  • (2022)Multilingual Document Concept Topic Modeling2022 European Conference on Natural Language Processing and Information Retrieval (ECNLPIR)10.1109/ECNLPIR57021.2022.00027(84-91)Online publication date: Jul-2022
  • (2019)Combining semantic graph and probabilistic topic models for discovering coherent topicsWeb Intelligence10.3233/WEB-19042417:4(365-379)Online publication date: 13-Nov-2019
  • (2018)25 years of quality management research – outlines and trendsInternational Journal of Quality & Reliability Management10.1108/IJQRM-01-2017-001335:1(208-231)Online publication date: 2-Jan-2018
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cover image ACM Conferences
CIKM '08: Proceedings of the 17th ACM conference on Information and knowledge management
October 2008
1562 pages
ISBN:9781595939913
DOI:10.1145/1458082
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 26 October 2008

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Author Tags

  1. ontologies
  2. semantic concepts
  3. statistical topic models
  4. unsupervised learning

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CIKM08
CIKM08: Conference on Information and Knowledge Management
October 26 - 30, 2008
California, Napa Valley, USA

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Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

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  • (2022)Multilingual Document Concept Topic Modeling2022 European Conference on Natural Language Processing and Information Retrieval (ECNLPIR)10.1109/ECNLPIR57021.2022.00027(84-91)Online publication date: Jul-2022
  • (2019)Combining semantic graph and probabilistic topic models for discovering coherent topicsWeb Intelligence10.3233/WEB-19042417:4(365-379)Online publication date: 13-Nov-2019
  • (2018)25 years of quality management research – outlines and trendsInternational Journal of Quality & Reliability Management10.1108/IJQRM-01-2017-001335:1(208-231)Online publication date: 2-Jan-2018
  • (2018)Interpretation of text patternsData Mining and Knowledge Discovery10.1007/s10618-018-0556-z32:4(849-884)Online publication date: 26-Dec-2018
  • (2018)Introducing semantic variables in mixed distance measuresKnowledge and Information Systems10.1007/s10115-013-0663-540:3(559-593)Online publication date: 30-Dec-2018
  • (2017)Conceptual annotation of text patternsComputational Intelligence10.1111/coin.1213333:4(948-979)Online publication date: 26-Jul-2017
  • (2017)Exploring research on quality and reliability management through text mining methodologyInternational Journal of Quality & Reliability Management10.1108/IJQRM-03-2015-003334:7(975-1014)Online publication date: 7-Aug-2017
  • (2017)Topic Modeling for Unsupervised Concept Extraction and Document RankingIntelligent Systems Technologies and Applications10.1007/978-3-319-68385-0_11(123-135)Online publication date: 21-Oct-2017
  • (2016)A Framework for Automatic Personalised Ontology Learning2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)10.1109/WI.2016.0025(105-112)Online publication date: Oct-2016
  • (2016)Discovering Coherent Topics with Entity Topic Models2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)10.1109/WI.2016.0015(26-33)Online publication date: Oct-2016
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