Functional Treatment of Bilingual Alignment and Its Application to Semantic Processing

  • Yoshihiko Nitta
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 144)

Abstract

In this paper we propose a functional treatment of bilingual alignment together with some new mathematical functions to describe sentential semantics transparently. We propose that every sentence is composed of two categories: kernel sentence and meta-sentence. Meta-sentence can be understood as a sentence structuring operator, while kernel sentence is a simple structured, mono-predicate sentence. Kernel sentence has obvious translation of canonical form, while meta-sentence represents logical-semantic structure of sentence which takes kernel sentence(s) as its dominating variable(s). From meta-sentences we can draw a lot of useful semantic information. We also show typical examples of meta-sentences obtained from typical Japanese short sentence known as “haiku”.

Keywords

Bilingual Alignment Meta-sentence Kernel Sentence Translation Semantic Processing Haiku 

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References

  1. 1.
    Bentivogli, L., Pianta, E.: Exploiting Parallel Texts in the Creation of Multilingual Semantically Annotated Resources: the MultiSemiCor Corpus. Natural Language En-gineering 11(3), 247–261 (2005)CrossRefGoogle Scholar
  2. 2.
    Church, K., Dagan, I., Gale, W., Fung, P., Satish, B., Helfman, J.: Aligning Parallel Texts: Do Methods Developed for English French Generalize to Asian Languages? In: Proceedings of the Pacific Asia Conference on Formal and Computational Linguistics (1993)Google Scholar
  3. 3.
    Kinyon, A.: A Language-Independent Shallow-Parser Compiler. In: Proc. 39th ACL Ann. Meeting (European Chapter), pp. 322–329 (2001)Google Scholar
  4. 4.
    Macklovitch, E., Marie-Louise, H.: Line ‘em up: Advances in Alignment Technology and Their Impact on Translation Support Tools. In: AMTA, pp. 145–156 (1996)Google Scholar
  5. 5.
    Mihalcea, R., Simard, M.: Parallel Texts. Natural Language Engineering 11(3), 239–246 (2005)CrossRefGoogle Scholar
  6. 6.
    Munday, J.: Introducing Translation Studies. Taylor & Francis (2009)Google Scholar
  7. 7.
    Nitta, Y.: Idiosyncratic Gap: A Tough Problem to Machine Translation. In: Proc. Comp. Linguistics, COLING 1986, ACL (Assoc. Comp. Ling.) (1986)Google Scholar
  8. 8.
    Nitta, Y.: Problems of Machine Translation: From a Viewpoint of Logical Semantics. Economic Review of Nihon University, Nihon University, Tokyo 72(2), 23–42 (2002)Google Scholar
  9. 9.
    Nitta, Y.: The Utility and Problem of Insufficient Machine Translation. Economic Review of Nihon University 80(4), 1–54 (2001)Google Scholar
  10. 10.
    Pim, A.: Exploring Translation Theories. Routledge, Taylor & Francis (2010)Google Scholar
  11. 11.
    Saraki, M., Nitta, Y.: The Semantic Classification of Verb Conjunction in the "Shite" Form. In: Proceedings of Spring IECEI Conference, IECEI, Japan (2005)Google Scholar
  12. 12.
    Saraki, M., Nitta, Y. (ed.): Regular Expression and Text Mining, Second Printing, Akashi-Shoten, 312 p (2008) (in Japanese)Google Scholar
  13. 13.
    William, A.G., Church, K.W.: A Program for Aligning Sentences in Bilingual Corpora. Computational Linguistics 19(3), 75–102 (1993)Google Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  • Yoshihiko Nitta
    • 1
  1. 1.Nihon UniversityChiyodaJapan

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