Marker-based chunking in eleven European languages for analogy-based translation

Kota Takeya, Yves Lepage

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

An example-based machine translation (EBMT) system based on proportional analogies requires numerous proportional analogies between linguistic units to work properly. Consequently, long sentences cannot be handled directly in such a framework. Cutting sentences into chunks would be a solution. Using different markers, we count the number of proportional analogies between chunks in 11 European languages. As expected, the number of proportional analogies between chunks found is very high. These results, and preliminary experiments in translation, are promising for the EBMT system that we intend to build.

Original languageEnglish
Title of host publicationHuman Language Technology Challenges for Computer Science and Linguistics - 5th Language and Technology Conference, LTC 2011, Revised Selected Papers
PublisherSpringer Verlag
Pages432-444
Number of pages13
ISBN (Print)9783319089577
DOIs
Publication statusPublished - 2014 Jan 1
Event5th Language and Technology Conference, LTC 2011 - Poznan, Poland
Duration: 2011 Nov 252011 Nov 27

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8387 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference5th Language and Technology Conference, LTC 2011
CountryPoland
CityPoznan
Period11/11/2511/11/27

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Takeya, K., & Lepage, Y. (2014). Marker-based chunking in eleven European languages for analogy-based translation. In Human Language Technology Challenges for Computer Science and Linguistics - 5th Language and Technology Conference, LTC 2011, Revised Selected Papers (pp. 432-444). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8387 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-08958-4_35