Adaptive Translation: Finding Interlingual Mappings Using Self-Organizing Maps

  • Timo Honkela
  • Sami Virpioja
  • Jaakko Väyrynen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5163)

Abstract

This paper presents a method for creating interlingual word-to-word or phrase-to-phrase mappings between any two languages using the self-organizing map algorithm. The method can be used as a component in a statistical machine translation system. The conceptual space created by the self-organizing map serves as a kind of interlingual representation. The specific problems of machine translation are discussed in some detail. The proposed method serves in alleviating two problems. The main problem addressed here is the fact that different languages divide the conceptual space differently. The approach can also help in dealing with lexical ambiguity.

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References

  1. 1.
    Moore, T., Carling, C.: The Limitations of Language. Macmillan Press, Houndmills (1988)Google Scholar
  2. 2.
    Honkela, T.: Philosophical aspects of neural, probabilistic and fuzzy modeling of language use and translation. In: Proceedings of IJCNN 2007, International Joint Conference on Neural Networks (2007)Google Scholar
  3. 3.
    Bowerman, M.: The origins of children’s spatial semantic categories: cognitive versus linguistic determinants. In: Gumperz, J., Levinson, S.C. (eds.) Rethinking linguistic relativity, pp. 145–176. Cambridge University Press, Cambridge (1996)Google Scholar
  4. 4.
    Berlin, B., Kay, P.: Basic Color Terms: Their Universality and Evolution. University of California Press (1991/1969)Google Scholar
  5. 5.
    Gärdenfors, P.: Conceptual Spaces. MIT Press, Cambridge (2000)Google Scholar
  6. 6.
    Ahrenberg, L., Andersson, M., Merkel, M.: A simple hybrid aligner for generating lexical correspondences in parallel texts. In: Proceedings of COLING-ACL 1998, pp. 29–35 (1992)Google Scholar
  7. 7.
    Brown, P.F., Pietra, S.A.D., Pietra, V.J.D., Mercer, R.L.: The mathematics of statistical machine translation: Parameter estimation. Computational Linguistics 19(2), 263–311 (1993)Google Scholar
  8. 8.
    Koehn, P., Och, F.J., Marcu, D.: Statistical phrase-based translation. In: NAACL 2003: Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, Morristown, NJ, USA, pp. 48–54. Association for Computational Linguistics (2003)Google Scholar
  9. 9.
    Zhang, R., Yamamoto, H., Paul, M., Okuma, H., Yasuda, K., Lepage, Y., Denoual, E., Mochihashi, D., Finch, A., Sumita, E.: The NiCT-ATR Statistical Machine Translation System for IWSLT 2006. In: Proceedings of the International Workshop on Spoken Language Translation, Kyoto, Japan, pp. 83–90 (2006)Google Scholar
  10. 10.
    Kohonen, T.: Self-organizing formation of topologically correct feature maps. Biological Cybernetics 43(1), 59–69 (1982)MATHCrossRefMathSciNetGoogle Scholar
  11. 11.
    Kohonen, T.: Self-Organizing Maps. Springer Series in Information Sciences, vol. 30. Springer, Heidelberg (2001)MATHGoogle Scholar
  12. 12.
    Kohonen, T., Honkela, T.: Kohonen network. Scholarpedia, p. 7421 (2007)Google Scholar
  13. 13.
    Charniak, E.: Statistical Language Learning. MIT Press, Cambridge (1993)Google Scholar
  14. 14.
    Ritter, H., Kohonen, T.: Self-organizing semantic maps. Biological Cybernetics 61(4), 241–254 (1989)CrossRefGoogle Scholar
  15. 15.
    Kaski, S.: Dimensionality reduction by random mapping: Fast similarity computation for clustering. In: Proceedings of IJCNN 1998, International Joint Conference on Neural Networks, vol. 1, pp. 413–418. IEEE Service Center, Piscataway (1998)Google Scholar
  16. 16.
    Koehn, P.: Europarl: A parallel corpus for statistical machine translation. In: Proceedings of the 10th Machine Translation Summit, Phuket, Thailand, pp. 79–86 (2005)Google Scholar
  17. 17.
    Li, P., Farkas, I.: A self-organizing connectionist model of bilingual processing. In: Bilingual sentence processing, pp. 59–85. North-Holland, Amsterdam (2002)CrossRefGoogle Scholar
  18. 18.
    Laaksonen, J., Viitaniemi, V.: Emergence of ontological relations from visual data with self-organizing maps. In: Proceedings of SCAI 2006, Scandinavian Conference on Artificial Intelligence, Espoo, Finland, pp. 31–38 (2006)Google Scholar
  19. 19.
    Harnad, S.: The symbol grounding problem. Physica D 42, 335–346 (1990)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Timo Honkela
    • 1
  • Sami Virpioja
    • 1
  • Jaakko Väyrynen
    • 1
  1. 1.Adaptive Informatics Research CentreHelsinki University of TechnologyEspooFinland

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