Detecting Large Concept Extensions for Conceptual Analysis

  • Louis Chartrand
  • Jackie C. K. Cheung
  • Mohamed Bouguessa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10358)


When performing a conceptual analysis of a concept, philosophers are interested in all forms of expression of a concept in a text—be it direct or indirect, explicit or implicit. In this paper, we experiment with topic-based methods of automating the detection of concept expressions in order to facilitate philosophical conceptual analysis. We propose six methods based on LDA, and evaluate them on a new corpus of court decision that we had annotated by experts and non-experts. Our results indicate that these methods can yield important improvements over the keyword heuristic, which is often used as a concept detection heuristic in many contexts. While more work remains to be done, this indicates that detecting concepts through topics can serve as a general-purpose method for at least some forms of concept expression that are not captured using naive keyword approaches.


Concept mining Topic models Conceptual analysis 



This work is supported by research grants from the Natural Sciences and Engineering Research Council of Canada (NSERC) and from the Social Sciences and Humanities Research Council of Canada (SSHRC).


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Louis Chartrand
    • 1
  • Jackie C. K. Cheung
    • 2
  • Mohamed Bouguessa
    • 1
  1. 1.Department of Computer ScienceUniversity of Quebec at MontrealMontrealCanada
  2. 2.School of Computer ScienceMcGill UniversityMontrealCanada

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