Exploring New Languages with HAIRCUT at CLEF 2005

  • Paul McNamee
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4022)

Abstract

JHU/APL has long espoused the use of language-neutral methods for cross-language information retrieval. This year we participated in the ad hoc cross-language track and submitted both monolingual and bilingual runs. We undertook our first investigations in the Bulgarian and Hungarian languages. In our bilingual experiments we used several non-traditional CLEF query languages such as Greek, Hungarian, and Indonesian, in addition to several western European languages. We found that character n-grams remain an attractive option for representing documents and queries in these new languages. In our monolingual tests n-grams were more effective than unnormalized words for retrieval in Bulgarian (+30%) and Hungarian (+63%). Our bilingual runs made use of subword translation, statistical translation of character n-grams using aligned corpora, when parallel data were available, and web-based machine translation, when no suitable data could be found.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Cavnar, W.B., Trenkle, J.M.: N-Gram Based Text Categorization. In: Proceedings of the Third Symposium on Document Analysis and Information Retrieval, pp. 161–169 (1994)Google Scholar
  2. 2.
    Church, K.W.: Char_align: A program for aligning parallel texts at the character level. In: Proceedings of the 31st Annual Meeting of the Association for Computational Linguistics, pp. 1–8 (1993)Google Scholar
  3. 3.
    Damashek, M.: Gauging Similarity with n-grams: Language-Independent Categorization of Text. Science 267, 843–848 (1995)CrossRefGoogle Scholar
  4. 4.
    Hiemstra, D.: Using Language Models for Information Retrieval. Ph. D. Thesis, Center for Telematics and Information Technology, The Netherlands (2000)Google Scholar
  5. 5.
    Jelinek, F., Mercer, R.: Interpolated Estimation of Markov Source Parameters from Sparse Data. In: Gelsema, E.S., Kanal, L.N. (eds.) Pattern Recognition in Practice, pp. 381–402. North-Holland, Amsterdam (1980)Google Scholar
  6. 6.
    Koehn, P.: Europarl: A multilingual corpus for evaluation of machine translation (unpublished) http://www.isi.edu/koehn/publications/europarl/
  7. 7.
    Mayfield, J., McNamee, P., Piatko, C.: The JHU/APL HAIRCUT System at TREC-8. In: Voorhees, E., Harman, D. (eds.) Proceedings of the Eighth Text REtrieval Conference (TREC-8), NIST Special Publication, Gaithersburg, Maryland, pp. 500–246 (2000)Google Scholar
  8. 8.
    Mayfield, J., McNamee, P.: Single N-gram Stemming. In: Proceedings of the 26th Annual International Conference on Research and Development in Information Retrieval (SIGIR 2003), Toronto, Ontario, pp. 415–416 (July 2003)Google Scholar
  9. 9.
    McNamee, P., Mayfield, J.: JHU/APL Experiments in Tokenization and Non-Word Translation. In: Working Notes of the CLEF 2003 Workshop, pp. 19-28 (2003)Google Scholar
  10. 10.
    McNamee, P., Mayfield, J.: Character N-gram Tokenization for European Language Text Retrieval. Information Retrieval 7(1-2), 73–97 (2004)CrossRefGoogle Scholar
  11. 11.
    McNamee, P., Mayfield, J.: Translating Pieces of Words. In: Proceedings of the 28th Annual International Conference on Research and Development in Information Retrieval (SIGIR 2005), Salvador, Brazil, pp. 643–644 (August 2005)Google Scholar
  12. 12.
    Mihalcea, R., Nastase, V.: Letter Level Learning for Language Independent Diacritics Restoration. In: Proceedings of the 6th Conference on Natural Language Learning (CoNLL 2002), pp. 105–111 (2002)Google Scholar
  13. 13.
    Pirkola, A., Hedlund, T., Keskusalo, H., Järvelin, K.: Dictionary-Based Cross-Language Information Retrieval: Problems, Methods, and Research Findings. Information Retrieval 4, 209–230 (2001)MATHCrossRefGoogle Scholar
  14. 14.
    Ponte, J.M., Croft, W.B.: A Language Modeling Approach to Information Retrieval. In: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Melbourne, Australia, pp. 275–281 (1998)Google Scholar
  15. 15.
    Zamora, E.M., Pollock, J.J., Zamora, A.: The Use of Trigram Analysis for Spelling Error Detection. Information Processing and Management 17, 305–316 (1981)CrossRefGoogle Scholar
  16. 16.

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Paul McNamee
    • 1
  1. 1.The Johns Hopkins University Applied Physics LaboratoryLaurelUSA

Personalised recommendations