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Analogies Between Binary Images: Application to Chinese Characters

  • Yves Lepage
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 548)

Abstract

The purpose of this chapter is to show how it is possible to efficiently extract the structure of a set of objects by use of the notion of proportional analogy. As a proportional analogy involves four objects, the very naïve approach to the problem, has basically a complexity of \(O(n^4)\) for a given set of \(n\) objects. We show, under some conditions on proportional analogy, how to reduce this complexity to \(O(n^2)\) by considering an equivalent problem, that of enumerating analogical clusters that are informative and not redundant. We further show how some improvements make the task tractable. We illustrate our technique with a task related with natural language processing, that of clustering Chinese characters. In this way, we re-discover the graphical structure of these characters.

Keywords

Analogy Analogical clusters Binary images Chinese characters 

References

  1. 1.
    Gentner, D.: Structure mapping: a theoretical model for analogy. Cogn. Sci. 7(2), 155–170 (1983)CrossRefGoogle Scholar
  2. 2.
    Lepage, Y.: Analogy and formal languages. In: Proceedings of FG/MOL 2001, Helsinki, pp. 1–12 (2001)Google Scholar
  3. 3.
    Yvon, F., Stroppa, N., Miclet, L., Delhay, A.: Solving analogical equations on words. Rapport Technique ENST2004D005, ENST (2004)Google Scholar
  4. 4.
    Hoffman, R.R.: Monster analogies. AI Mag. 11, 11–35 (1995)Google Scholar
  5. 5.
    Lepage, Y.: Lower and higher estimates of the number of “true analogies” between sentences contained in a large multilingual corpus. In: Proceedings of COLING-2004, vol. 1, pp. 736–742. Geneva (2004)Google Scholar
  6. 6.
    Lepage, Y., Migeot, J., Guillerm, E.: A corpus study on the number of true proportional analogies between chunks in two typologically different languages. In: Proceedings of the Seventh International Symposium on Natural Language Processing (SNLP 2007), pp. 117-122. Kasetsart University, Pattaya, Thailand, ISBN 978-974-623-062-9 (2007)Google Scholar
  7. 7.
    Lepage, Y., Migeot, J., Guillerm, E.: A measure of the number of true analogies between chunks in Japanese. Lect. Notes Artif. Intell. 5603, 154–164 (2009)Google Scholar
  8. 8.
    Veale, T., Chen, S.: Learning to extract semantic content from the orthographic structure of Chinese words. In: Proceedings of the 17th Irish Conference on Artificial Intelligence and Cognitive Science (AICS2006) (2006)Google Scholar
  9. 9.
    Paul, H.: Prinzipien der Sprachgeschichte. Niemayer, Tübingen (1920)Google Scholar
  10. 10.
    Varro, M.T.: De lingua latina. Coll. Belles-lettres. Trad. J. Collart., Paris (1954)Google Scholar
  11. 11.
    Turney, P.D., Littman, M.L.: Corpus-based learning of analogies and semantic relations. Mach. Learn. 60(1–3), 251–278 (2005)CrossRefGoogle Scholar
  12. 12.
    Turney, P.D.: Similarity of semantic relations. Comput. Linguist. 32(2), 379–416 (2006)CrossRefMATHGoogle Scholar
  13. 13.
    Turney, P.: A uniform approach to analogies, synonyms, antonyms, and associations. In: Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008), pp. 905–912. Coling 2008 Organizing Committee, Manchester, UK (2008)Google Scholar
  14. 14.
    Itkonen, E.: Iconicity, analogy, and universal grammar. J. Pragmat. 22(1), 37–53 (1994)CrossRefGoogle Scholar
  15. 15.
    Yencken, L., Baldwin, T.: Measuring and predicting orthographic associations: modelling the similarity of Japanese kanji. In: Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008), pp. 1041–1048. Coling 2008 Organizing Committee, Manchester, UK (2008)Google Scholar
  16. 16.
    Matsushita, K., Lepage, Y.: Rediscovering the structure of Chinese characters using analogy-based methods (in Japanese). In: Proceedings of the 18th Japanese National Conference in Natural Language Processing, pp. 438–441. Nagoya (2013)Google Scholar
  17. 17.
    Lepage, Y.: Analogy and formal languages. Electron. Notes Theor. Comput. Sci. 53, 180–191 (2004)Google Scholar
  18. 18.
    Lepage, Y.: Of that kind of analogies capturing linguistic commutations (in French). Habilitation thesis, Joseph Fourier Grenoble University (2003)Google Scholar
  19. 19.
    Lepage, Y., Goh, C.: Towards automatic acquisition of linguistic features. In: Jokinen, K., Bick, E. (eds.) Proceedings of the 17th Nordic Conference on Computational Linguistics (NODALIDA 2009), pp. 118–125. Odense (2009)Google Scholar
  20. 20.
    Langlais, P., Yvon, F.: Scaling up analogical learning. In: Coling 2008: Companion Volume: Posters, pp. 51–54. Coling 2008 Organizing Committee, Manchester, UK (2008)Google Scholar
  21. 21.
    Finkel, R., Bentley, J.: Quad trees: a data structure for retrieval on composite keys. Acta Informatica 4(1), 1–9 (1974)CrossRefMATHGoogle Scholar
  22. 22.
    Lepage, Y., Gosme, J., Lardilleux, A.: Estimating the proximity between languages by their commonality in vocabulary structures. In: Lecture Notes in Artificial Intelligence Human Language Technology—Challenges for Computer Science and Linguistics, pp. 127–138. (2011)Google Scholar
  23. 23.
    Lepage, Y., Denoual, E.: Purest ever example-based machine translation: detailed presentation and assessment. Mach. Transl. 19, 251–282 (2005)CrossRefGoogle Scholar
  24. 24.
    Lepage, Y., Denoual, E.: Automatic generation of paraphrases to be used as translation references in objective evaluation measures of machine translation. In: Proceedings of the Third International Workshop on Paraphrasing (IWP 2005), pp. 57–64. Jeju (2005)Google Scholar
  25. 25.
    Lepage, Y.: Languages of analogical strings. In: Proceedings of COLING-2000, vol. 1, pp. 488–494. Saarbrücken (2000)Google Scholar
  26. 26.
    Croft, W.: Radical Construction Grammar: Syntactic Theory in Typological Perspective. Oxford Linguistics. Oxford University Press, Oxford (2001)Google Scholar
  27. 27.
    Itkonen, E.: Analogy as structure and process: approaches in linguistics, cognitive psychology and philosophy of science. In: Dascal, M., Gibbs, R.W., Nuyts, J. (eds.) Human Cogntive Processing, vol. 14, p. 250. John Benjamins Publishing Company, Amsterdam/Philadelphia (2005)Google Scholar
  28. 28.
    Hofstadter, D.: The Fluid Analogies Research Group: Fluid Concepts and Creative Analogies. Basic Books, New York (1994)Google Scholar
  29. 29.
    Lepage, Y.: Solving analogies on words: an algorithm. In: Proceedings of COLING-ACL’98, vol. I, pp. 728–735. Montréal (1998)Google Scholar
  30. 30.
    Correa, W., Prade, H., Richard, G.: When intelligence is just a matter of copying. In: Proceedings of the 20th European Conference on Artificial Intelligence (ECAI 2012), pp. 276–281 (2012)Google Scholar
  31. 31.
    Matsushita, K.: Data processing of Chinese Hanzi by proportional analogy and verification of learning efficiency by subjects (in Japanese). Master’s thesis, Graduate School of Information, Production and Systems, Waseda University (2013)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Graduate School of Information, Production and SystemsWaseda UniversityKitakyushu-shiJapan

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