Harvesting Regional Transliteration Variants with Guided Search

  • Jin-Shea Kuo
  • Haizhou Li
  • Chih-Lung Lin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5459)

Abstract

This paper proposes a method to harvest regional transliteration variants with guided search. We first study how to incorporate transliteration knowledge into query formulation so as to significantly increase the chance of desired transliteration returns. Then, we study a cross-training algorithm, which explores valuable information across different regional corpora for the learning of transliteration models to in turn improve the overall extraction performance. The experimental results show that the proposed method not only effectively harvests a lexicon of regional transliteration variants but also mitigates the need of manual data labeling for transliteration modeling. We also conduct an inquiry into the underlying characteristics of regional transliterations that motivate the cross-training algorithm.

Keywords

transliteration regional transliteration variants cross-training algorithm guided search constraint-based exploration 

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References

  1. 1.
    Cheng, P.-J., Lu, W.-H., Tien, J.-W., Chien, L.-F.: Creating Multilingual Translation Lexicons with Regional Variations Using Web Corpora. In: Proc. of 42nd ACL, pp. 534–541 (2004)Google Scholar
  2. 2.
    Kwong, O.Y., Tsou, B.K.: Regional Variation of Domain-Specific Lexical Items: Toward a Pan-Chinese Lexical Resource. In: Proc. of 5th SIGHAN Workshop on Chinese Language Processing, pp. 9–16 (2006)Google Scholar
  3. 3.
    Li, H., Sim, K.C., Kuo, J.-S., Dong, M.: Semantic Transliteration of Personal Names. In: Proc. of 45th ACL, pp. 120–127 (2007)Google Scholar
  4. 4.
    Knight, K., Graehl, J.: Machine Transliteration. Computational Linguistics 24(4), 599–612 (1998)Google Scholar
  5. 5.
    Li, H., Zhang, M., Su., J.: A Joint Source Channel Model for Machine Transliteration. In: Proc. of 42nd ACL, pp. 159–166 (2004)Google Scholar
  6. 6.
    Oh, J.-H., Choi, K.-S.: An Ensemble of Grapheme and Phoneme for Machine Transliteration. In: Proc. of 2nd IJCNLP, pp. 450–461 (2005)Google Scholar
  7. 7.
    Hermjakob, U., Knight, K., Daumé III, H.: Name Translation in Statistical Machine Translation Learning When to Transliterate. In: Proc. of 46th ACL, pp. 389–397 (2008)Google Scholar
  8. 8.
    Meng, H., Lo, W.-K., Chen, B., Tang, T.: Generate Phonetic Cognates to Handle Name Entities in English-Chinese Cross-language Spoken Document Retrieval. In: Proc. of the IEEE workshop on ASRU, pp. 311–314 (2001)Google Scholar
  9. 9.
    Brill, E., Kacmarcik, G., Brockett, C.: Automatically Harvesting Katakana-English Term Pairs from Search Engine Query Logs. In: Proc. of NLPPRS, pp. 393–399 (2001)Google Scholar
  10. 10.
    Kuo, J.-S., Li, H., Yang, Y.-K.: A Phonetic Similarity Model for Automatic Extraction of Transliteration Pairs. ACM TALIP 6(2), 1–24 (2007)Google Scholar
  11. 11.
    Nie, J.-Y., Isabelle, P., Simard, M., Durand, R.: Cross-language Information Retrieval based on Parallel Texts and Automatic Mining of Parallel Text from the Web. In: Proc. of 22nd ACM SIGIR, pp. 74–81 (1999)Google Scholar
  12. 12.
    Sproat, R., Tao, T., Zhai, C.: Named Entity Transliteration with Comparable Corpora. In: Proc. of 44th ACL, pp. 73–80 (2006)Google Scholar
  13. 13.
    Lin, D., Zhao, S., Durme, B., Pasca, M.: Mining Parenthetical Translations from the Web by Word Alignment. In: Proc. of 46th ACL, pp. 994–1002 (2008)Google Scholar
  14. 14.
    Chang, M.-W., Ratinov, L., Roth, D.: Guiding Semi-Supervision with Constraint-Driven Learning. In: Proc. of 45th ACL, pp. 280–287 (2007)Google Scholar
  15. 15.
    Sarawagi, S., Chakrabarti, S., Godboley, S.: Cross-training: Learning Probabilistic Mappings between Topics. In: Proc. of SIGKDD 2003, pp. 177–186 (2003)Google Scholar
  16. 16.
    Soonthornphisaj, N., Kijsirikul, B.: Iterative Cross-training: An Algorithm for Learning from Unlabeled Web Pages. International Journal of Intelligent Systems 19(1-2), 131–147 (2004)CrossRefGoogle Scholar
  17. 17.
    Brin, S., Page, L.: The Anatomy of a Large-scale Hypertextual Web Search Engine. In: Proc. of 7th WWW, pp. 107–117 (1998)Google Scholar
  18. 18.
    Chakrabarti, S., Berg, M., Dom, B.: Focused Crawling: A New Approach to Topic-Specific Web Resource Discovery. In: Proc. of 8th WWW, pp. 545–562 (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Jin-Shea Kuo
    • 1
  • Haizhou Li
    • 2
  • Chih-Lung Lin
    • 3
  1. 1.Chung-Hwa Telecomm. Labs.TaoyuanTaiwan
  2. 2.Institute for Infocomm ResearchSingapore
  3. 3.Chung Yuan Christian UniversityTaoyuanTaiwan

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