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International Conference on Theory and Practice of Digital Libraries

TPDL 2019: Digital Libraries for Open Knowledge pp 18–32Cite as

An Unsupervised Method for Concept Association Analysis in Text Collections

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11799))

Abstract

This paper addresses the challenge of content categorization to support document navigation and retrieval. The work is motivated by the need to categorize all legislation of a country, where the existing metadata for each document is not sufficient for effective categorization, as concepts vary considerably among documents, resulting in highly sparse vector-space models. To address this challenge, we survey recent related work and propose a solution that integrates currently dispersed principles in a new unsupervised knowledge discovery process combining principles from topic modeling and formal concept analysis, thus not requiring prior domain knowledge to be applied in large document collections. The results confirm the potential of the proposed approach.

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Notes

  1. 1.

    https://dre.pt/.

  2. 2.

    http://trec.nist.gov/data.html.

  3. 3.

    https://dre.pt/web/guest/home/-/dre/3535010/details/maximized.

  4. 4.

    http://latviz.loria.fr/.

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Acknowledgement

This work was supported by Imprensa Nacional Casa da Moeda (INCM) and national funds through Fundação para a Ciência e a Tecnologia (FCT) with reference UID/CEC/50021/2019.

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Authors and Affiliations

  1. Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal

    Pavlo Kovalchuk, José Borbinha & Rui Henriques

  2. INESC-ID, Lisbon, Portugal

    Pavlo Kovalchuk, Diogo Proença, José Borbinha & Rui Henriques

Authors
  1. Pavlo Kovalchuk
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  2. Diogo Proença
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  3. José Borbinha
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  4. Rui Henriques
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Corresponding author

Correspondence to Pavlo Kovalchuk .

Editor information

Editors and Affiliations

  1. University of La Rochelle, La Rochelle, France

    Antoine Doucet

  2. VU University Amsterdam, Amsterdam, The Netherlands

    Antoine Isaac

  3. Linnaeus University, Växjö, Sweden

    Koraljka Golub

  4. OsloMet – Oslo Metropolitan University, Oslo, Norway

    Trond Aalberg

  5. Kyoto University, Kyoto, Japan

    Adam Jatowt

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Kovalchuk, P., Proença, D., Borbinha, J., Henriques, R. (2019). An Unsupervised Method for Concept Association Analysis in Text Collections. In: Doucet, A., Isaac, A., Golub, K., Aalberg, T., Jatowt, A. (eds) Digital Libraries for Open Knowledge. TPDL 2019. Lecture Notes in Computer Science(), vol 11799. Springer, Cham. https://doi.org/10.1007/978-3-030-30760-8_2

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  • DOI: https://doi.org/10.1007/978-3-030-30760-8_2

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  • Print ISBN: 978-3-030-30759-2

  • Online ISBN: 978-3-030-30760-8

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