Behavior Research Methods

, Volume 40, Issue 4, pp 926–934 | Cite as

Testing the cognitive relevance of a geometric model on a word association task: A comparison of humans, ACOM, and LSA

  • Hyngsuk Ji
  • Benoît Lemaire
  • Hyunseung Choo
  • Sabine Ploux
Article

Abstract

The general aim of this study is to validate the cognitive relevance of the geometric model used in the semantic atlases (SA). With this goal in mind, we compare the results obtained by the automatic contexonym organizing model (ACOM)—an SA-derived model for word sense representation based on contextual links—with human subjects’ responses on a word association task. We begin by positioning the geometric paradigm with respect to the hierarchical paradigm (WordNet) and the vector paradigm (latent semantic analysis [LSA] and the hyperspace analogue to language model). Then we compare ACOM’s responses with Hirsh and Tree’s (2001) word association norms based on the responses of two groups of subjects. The results showed that words associated by 50% or more of the Hirsh and Tree subjects were also proposed by ACOM (e.g., 71% of the words in the norms were also given by ACOM). Finally, we compare ACOM and LSA on the basis of the same association norms. The results indicate better performance for the geometric model.

References

  1. Benzécri, J.-P. (1980). L’analyse des données: L’analyse des correspondances. Paris: Bordas.Google Scholar
  2. Burgess, C., & Lund, K. (1997). Modelling parsing constraints with high-dimensional context space. Language & Cognitive Processes, 12, 177–210.CrossRefGoogle Scholar
  3. Collins, A., & Loftus, E. (1975). A spreading activation theory of semantic memory. Psychological Review, 82, 407–428.CrossRefGoogle Scholar
  4. Collins, A., & Quillian, M. (1969). Does category size affect categorization time? Journal of Verbal Learning & Verbal Behavior, 8, 240–248.CrossRefGoogle Scholar
  5. Fellbaum, C. (Ed.) (1998). WordNet: An electronic lexical database. Cambridge, MA: MIT Press.Google Scholar
  6. Forde, E. M. E., & Humphreys, G. W. (Eds.) (2002). Category specificity in brain and mind. Hove, U.K.: Psychology Press.Google Scholar
  7. Gärdenfors, P. (2000). Conceptual spaces: The geometry of thought. Cambridge, MA: MIT Press.Google Scholar
  8. Griffiths, T. L., & Steyvers, M. (2003). Prediction and semantic association. In S. Becker, S. Thrun, & K. Obermayer (Eds.), Advances in neural information processing systems 15 (pp. 11–18). Cambridge, MA: MIT Press.Google Scholar
  9. Hirsh, C., & Tree, J. (2001). Word association norms for two cohorts of British adults. Journal of Neurolinguistics, 14, 1–44.CrossRefGoogle Scholar
  10. Ji, H., Ploux, S., & Wehrli, E. (2003). Lexical knowledge representation with contexonyms. In Proceedings of the 9th Machine Translation Summit (pp. 194-201).Google Scholar
  11. Kintsch, W. (2001). Predication. Cognitive Science, 25, 173–202.CrossRefGoogle Scholar
  12. Laham, D. (1997). Latent semantic analysis approaches to categorization. In M.-G. Shafto & P. Langley (Eds.), Proceedings of the 19th Annual Meeting of the Cognitive Science Society. Mahwah, NJ: Erlbaum.Google Scholar
  13. Landauer, T. K., McNamara, D. S., Dennis, S., & Kintsch, W. (2007). Handbook of latent semantic analysis. Mahwah, NJ: Erlbaum.Google Scholar
  14. Lund, K., & Burgess, C. (1996). Producing high-dimensional semantic spaces from lexical co-occurrence. Behavior Research Methods, Instruments, & Computers, 28, 203–208.CrossRefGoogle Scholar
  15. The New Shorter Oxford English Dictionary (1999). Oxford: Oxford University Press.Google Scholar
  16. Ploux, S. (1997). Modélisation et traitement informatique de la synonymie. Linguisticae Investigationes, 21, 1–28.CrossRefGoogle Scholar
  17. Ploux, S., & Victorri, B. (1998). Construction d’espaces sémantiques à l’aide de dictionnaires informatisés des synonymes. Traitement Automatique des Langues, 39, 161–182.Google Scholar
  18. Pottier, B. (1992). Sémantique générale. Paris: PUF.Google Scholar
  19. Rips, L. J., Shoben, E. J., & Smith, E. E. (1973). Semantic distance and the verification of semantic relations. Journal of Verbal Learning & Verbal Behavior, 81, 1–20.CrossRefGoogle Scholar
  20. Smith, E. E., Shoben, E. J., & Rips, L. J. (1974). Structure and process in semantic memory: A featural model for semantic decisions. Psychological Review, 81, 214–241.CrossRefGoogle Scholar
  21. Vigliocco, G., Vinson, D., Lewis, W., & Garrett, M. (2004). Representing the meanings of object and action words: The featural and unitary semantic system hypothesis. Cognitive Psychology, 48, 422–488.PubMedCrossRefGoogle Scholar
  22. Widdows, D., & Higgins, M. (2004). Geometric ordering of concepts, logical disjunction, and learning by induction. In S. D. Levy & R. Gayler (Eds.), Compositional connectionism in cognitive science (AAAI Fall Symposium Series). Menlo Park, CA: AAAI Press.Google Scholar
  23. Wittgenstein, L. (1953). Philosophical investigations. Oxford: Blackwell.Google Scholar

Copyright information

© Psychonomic Society, Inc. 2008

Authors and Affiliations

  • Hyngsuk Ji
    • 1
  • Benoît Lemaire
    • 2
  • Hyunseung Choo
    • 1
  • Sabine Ploux
    • 3
  1. 1.Sungkyunkwan UniversitySeoulKorea
  2. 2.Laboratoire TIMC-IMAGUniversity of GrenobleGrenobleFrance
  3. 3.Institut des Sciences Cognitives, L2C2, UMR5230 CNRSUniversité Claude Bernard Lyon 1Bron CedexFrance

Personalised recommendations