Latent Variable Models for Causal Knowledge Acquisition

  • Takashi Inui
  • Hiroya Takamura
  • Manabu Okumura
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4394)


We describe statistical models for detecting causality between two events. Our models are kinds of latent variable models, actually expanded versions of the existing statistical co-occurrence models. The (statistical) dependency information between two events needs to be incorporated into causal models. We handle this information via latent variables in our models. Through experiments, we achieved .678 F-measure value for the evaluation data.


Causal Relation Causal Model Unlabeled Data Latent Variable Model Event Pair 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Allen, J.F.: Recognizing intentions from natural language utterances. In: Brady, M., Berwick, R.C. (eds.) Computational models of discourse, MIT Press, Cambridge (1983)Google Scholar
  2. 2.
    Chang, D.-S., Choi, K.-S.: Causal relation extraction using cue phrase and lexical pair probabilities. In: Proceedings of the 1st International Joint Conference of Natural Language Processing (IJCNLP-2004), pp. 61–70 (2004)Google Scholar
  3. 3.
    Church, K.W., Hanks, P.: Word association norms, mutual information, and lexicography. In: Proceedings of the 27th. Annual Meeting of the Association for Computational Linguistics (ACL-1989), pp. 76–83 (1989)Google Scholar
  4. 4.
    Cooper, G., Heckerman, D., Meek, C.: A Bayesian approach to causal discovery. Technical report, Microsoft Research Advanced Technology Division, Microsoft Corporation, Technical Report MSR-TR-97-05 (1997)Google Scholar
  5. 5.
    Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B 34, 1–38 (1977)MathSciNetzbMATHGoogle Scholar
  6. 6.
    Fellbaum, C.: WordNet: An Electronic Lexical Database. MIT Press, Cambridge (1998)zbMATHGoogle Scholar
  7. 7.
    Garcia, D.: COATIS, an NLP system to locate expressions of actions connected by causality links. In: Proceedings of The 10th European Knowledge Acquisition Workshop, pp. 347–352 (1997)Google Scholar
  8. 8.
    Girju, R.: Automatic detection of causal relations for question answering. In: Proceedings of Annual Meeting of the Association for Computational Linguistics, Workshop on Multilingual Summarization and Question Answering - Machine Learning and Beyond (2003)Google Scholar
  9. 9.
    Girju, R., Moldovan, D.: Mining answers for causation questions. In: Proc. The AAAI Spring Symposium on Mining Answers from Texts and Knowledge Bases (2002)Google Scholar
  10. 10.
    Hobbs, J.R.: Coherence and co-reference. Cognitive Science 1, 67–82 (1979)CrossRefGoogle Scholar
  11. 11.
    Hobbs, J.R.: On the coherence and structure of discourse. Technical report, Technical Report CSLI-85-37, Center for The Study of Language and Information (1985)Google Scholar
  12. 12.
    Hofmann, T.: Unsupervised learning by probabilistic latent semantic analysis. Machine Learning Journal 42(1), 177–196 (2001)MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Hofmann, T., Puzicha, J.: Statistical models for co-occurrence data. Technical Report AIM-1625, Artificial Intelligence Laboratory, MIT (1998)Google Scholar
  14. 14.
    Inui, T., Inui, K., Matsumoto, Y.: What kinds and amounts of causal knowledge can be acquired from text by using connective markers as clues? In: Grieser, G., Tanaka, Y., Yamamoto, A. (eds.) DS 2003. LNCS (LNAI), vol. 2843, pp. 180–193. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  15. 15.
    Inui, T., Okumura, M.: Investigating the characteristics of causal relations in Japanese text. In: Proceedings of Annual Meeting of the Association for Computational Linguistics, Workshop on Frontiers in Corpus Annotation II: Pie in the Sky, pp. 37–44 (2005)Google Scholar
  16. 16.
    Khoo, C.S.G., Chan, S., Niu, Y.: Extracting causal knowledge from a medical database using graphical patterns. In: Proceedings of The 38th. Annual Meeting of The Association for Computational Linguistics (ACL-2000), pp. 336–343 (2000)Google Scholar
  17. 17.
    Mainichi. Mainichi Shimbun CD-ROM version (1995)Google Scholar
  18. 18.
    Mitchell, T.: Machine Learning. McGraw-Hill, New York (1997)zbMATHGoogle Scholar
  19. 19.
    Nigam, K., McCallum, A.K., Thrun, S., Mitchell, T.M.: Text classification from labeled and unlabeled documents using EM. Machine Learning 39(2/3), 103–134 (2000)CrossRefzbMATHGoogle Scholar
  20. 20.
    Pearl, J.: Causality: Models, Reasoning, and Inference. Cambridge Universiy Press, Cambridge (2000)zbMATHGoogle Scholar
  21. 21.
    Pei, J., Han, J., Mortazavi-Asi, B., Pinto, H., Chen, Q., Dayal, U., Hsu, M.-C.: PrefixSpan: mining sequential patterns efficiently by prefix projected pattern growth. In: Proceedings of 1st Conference of Data Enginnering (ICDE-2001), pp. 215–226 (2001)Google Scholar
  22. 22.
    Pustejovsky, J.: The Generative Lexicon. MIT Press, Cambridge (1995)Google Scholar
  23. 23.
    Sanchez-Graillet, O., Poesio, M.: Acquiring bayesian networks from text. In: Proceedings of the 4th International Conference on Language Resources and Evaluation (LREC-2004), pp. 955–958 (2004)Google Scholar
  24. 24.
    Terada, A.: A Study of Text Mining Techniques Using Natural Language Processing. PhD thesis, Tokyo Institute of Technology (2003)Google Scholar
  25. 25.
    Torisawa, K.: An unsupervised learning method for commonsensical inference rules on events. In: Proceedings of the Second CoLogNet-ElsNET Symposium, pp. 146–153 (2003)Google Scholar
  26. 26.
    U.S. National Library of Medicine: The MEDLINE database (2001), see also,
  27. 27.
    Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, Heidelberg (1995)CrossRefzbMATHGoogle Scholar
  28. 28.
    Zhang, N.L., Nielsen, T.D., Jensen, F.V.: Latent variable discovery in classification models. Artificial Intelligence in Medicine 30(3), 283–299 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Takashi Inui
    • 1
  • Hiroya Takamura
    • 2
  • Manabu Okumura
    • 2
  1. 1.Integrated Research Institute, Tokyo Institute of Technology, 4259, Nagatsuta, Midori-ku, Yokohama, 226-8503Japan
  2. 2.Precision and Intelligence Laboratory, Tokyo Institute of Technology, 4259, Nagatsuta, Midori-ku, Yokohama, 226-8503Japan

Personalised recommendations