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)

Abstract

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.

Keywords

Concept mining Topic models Conceptual analysis 

References

  1. 1.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)MATHGoogle Scholar
  2. 2.
    Blondel, M.: Latent Dirichlet Allocation in Python (2010). https://gist.github.com/mblondel/542786
  3. 3.
    Braddon-Mitchell, D., Nola, R.: Introducing the Canberra plan. In: Braddon-Mitchell, D., Nola, R. (eds.) Conceptual Analysis and Philosophical Naturalism, pp. 1–20. MIT Press (2009)Google Scholar
  4. 4.
    Chalmers, D.J., Jackson, F.: Conceptual analysis and reductive explanation. Philos. Rev. 110(3), 315–361 (2001)CrossRefGoogle Scholar
  5. 5.
    Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by latent semantic analysis. J. Am. Soc. Inf. Sci. 41(6), 391 (1990)CrossRefGoogle Scholar
  6. 6.
    Dutilh Novaes, C., Reck, E.: Carnapian explication, formalisms as cognitive tools, and the paradox of adequate formalization. Synthese 194(1), 195–215 (2017)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: From data mining to knowledge discovery in databases. AI Magazine 17(3), 37 (1996)Google Scholar
  8. 8.
    Griffiths, T.L., Steyvers, M.: Finding scientific topics. Proc. Natl. Acad. Sci. 101(suppl 1), 5228–5235 (2004)CrossRefGoogle Scholar
  9. 9.
    Gwet, K.L.: Computing inter-rater reliability and its variance in the presence of high agreement. Br. J. Math. Stat. Psychol. 61(1), 29–48 (2008)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Haslanger, S.: Resisting Reality: Social Construction and Social Critique. Oxford University Press, Oxford (2012)CrossRefGoogle Scholar
  11. 11.
    Hoffman, M., Bach, F.R., Blei, D.M.: Online learning for latent Dirichlet allocation. In: Advances in Neural Information Processing Systems, pp. 856–864 (2010)Google Scholar
  12. 12.
    Jackson, F.: From Metaphysics to Ethics: A Defence of Conceptual Analysis. Oxford University Press, New York (1998)Google Scholar
  13. 13.
    Knobe, J., Nichols, S.: An experimental philosophy manifesto. In: Knobe, J., Nichols, S. (eds.) Experimental philosophy, pp. 3–14. Oxford University Press (2008)Google Scholar
  14. 14.
    Laurence, S., Margolis, E.: Concepts and conceptual analysis. Philos. Phenomenological Res. 67(2), 253–282 (2003)CrossRefGoogle Scholar
  15. 15.
    Meunier, J.G., Biskri, I., Forest, D.: Classification and categorization in computer assisted reading and analysis of texts. In: Lefebvre, C., Cohen, H. (eds.) Handbook of Categorization in Cognitive Science, pp. 955–978. Elsevier (2005)Google Scholar
  16. 16.
    Řehůřek, R., Sojka, P.: software framework for topic modelling with large corpora. In: Proceedings of LREC 2010 workshop New Challenges for NLP Frameworks, pp. 46–50. University of Malta, Valletta, Malta (2010). http://www.fi.muni.cz/usr/sojka/presentations/lrec2010-poster-rehurek-sojka.pdf
  17. 17.
    Schmid, H.: Probabilistic part-of-speech tagging using decision trees. In: Proceedings of the International Conference on New Methods in Language Processing, vol. 12, pp. 44–49 (1994)Google Scholar

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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