Automatic Acquisition of Adjacent Information and Its Effectiveness in Extraction of Bilingual Word Pairs from Parallel Corpora

  • Hiroshi Echizen-ya
  • Kenji Araki
  • Yoshio Momouchi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3513)


We propose a learning method for solving the sparse data problem in automatic extraction of bilingual word pairs from parallel corpora. In general, methods based on similarity measures are insufficient because of the sparse data problem. The essence of our method is the use of this inference: in local parts of bilingual sentence pairs (e.g., phrases, not sentences), the equivalents of words that adjoin the source language words of bilingual word pairs also adjoin the target language words of bilingual word pairs. Our learning method automatically acquires such adjacent information. The acquired adjacent information is used to extract bilingual word pairs. As a result, our system can limit the search scope for the decision of equivalents in bilingual sentence pairs by extracting only word pairs that adjoin the acquired adjacent information. We applied our method to two systems based on Yates’ χ 2 and AIC. Results of evaluation experiments indicate that the extraction rates respectively improved 6.1 and 6.0 percentage points using our method.


Extraction Rate Word Pair Machine Translation Parallel Corpus Search Scope 
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  1. 1.
    Manning, C.D., Schütze, H.: Foundations of Statistical Natural Language Processing. MIT Press, Cambridge (1999)zbMATHGoogle Scholar
  2. 2.
    Echizen-ya, H., Araki, K., Momouchi, Y., Tochinai, K.: Study of Practical Effectiveness for Machine Translation Using Recursive Chain-link-type Learning. In: Proceedings of COLING 2002, pp. 246–252 (2002)Google Scholar
  3. 3.
    Hisamitsu, T., Niwa, Y.: Topic-Word Selection Based on Combinatorial Probability. In: NLPRS 2001, pp. 289–296 (2001)Google Scholar
  4. 4.
    Akaike, H.: A New Look at the Statistical Model Identification. IEEE Transactions on Automatic Control AC-19, 716–723 (1974)CrossRefMathSciNetGoogle Scholar
  5. 5.
    Utsuro, T., Hino, K., Kida, M.: Integrating Cross-Lingually Relevant News Articles and Monolingual Web Documents in Bilingual Lexicon Acquisition. In: Proceedings of COLING 2004, pp. 1036–1042 (2004)Google Scholar
  6. 6.
    Kaji, H., Aizono, T.: Extracting Word Correspondences from Bilingual Corpora Based on Word Co-occurrence Information. In: Proceedings of COLING 1996, pp. 23–28 (1996)Google Scholar
  7. 7.
    McTait, K., Trujillo, A.: A Language-Neutral Sparse-Data Algorithm for Extracting Translation Patterns. In: Proceedings of TMI 1999, pp. 98–108 (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Hiroshi Echizen-ya
    • 1
  • Kenji Araki
    • 2
  • Yoshio Momouchi
    • 1
  1. 1.Dept. of Electronics and InformationHokkai-Gakuen UniversitySapporoJapan
  2. 2.Graduate School of Information Science and TechnologyHokkaido UniversitySapporoJapan

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