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Normalizing German and English Inflectional Morphology to Improve Statistical Word Alignment

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 3265)


German has a richer system of inflectional morphology than English, which causes problems for current approaches to statistical word alignment. Using Giza++ as a reference implementation of the IBM Model 1, an HMMbased alignment and IBM Model 4, we measure the impact of normalizing inflectional morphology on German-English statistical word alignment. We demonstrate that normalizing inflectional morphology improves the perplexity of models and reduces alignment errors.


  • Noun Phrase
  • Machine Translation
  • Target Language
  • Source Language
  • Morphological Processing

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  • Hiyan, A., Douglas, S., Bangalore, S.: Learning de pendency translation models as collections of finite-state head transducers. Computational Linguistics 26(1), 45–60 (2000)

    CrossRef  MathSciNet  Google Scholar 

  • Brill, E.: Transformation-based error-driven learning and natural language processing: A case-study in part of speech tagging. Computational Linguistics 21(4), 543–565 (1995)

    Google Scholar 

  • Brown, Peter, F., Pietra, S.A.D., Pietra, V.J.D., Mercer, R.L.: The mathematics of statistical machine translation: Parameter estimation. Computational Linguistics 19(2), 263–311

    Google Scholar 

  • Dejean, A., Herve, A., Gaussier, E., Goutte, C., Yamada, K.: Reducing Parameter Space for Word Alignment. In: Proceedings from the HLT-NAACL, workshop on Building Parallel Texts,pp. 23-26 (2003)

    Google Scholar 

  • Gamon, M., Ringger, E., Zhang, Z., Moore, R., Corston-Oliver, S.: Extraposition: A case study in German sentence realization. In: Proceedings of COLING 2002, pp. 301–307 (2002)

    Google Scholar 

  • Goodman, J.: A Bit of Progress in Language Modeling, Extended Version. Microsoft Research Technical Report MSR-TR-2001-72 (2001)

    Google Scholar 

  • George, H.: Intelligent Writing Assistance. In: R. Dale, H. Moisl and H. Somers (eds.), Handbook of Natural Language Processing. Marcel Dekker ,New York (2000)

    Google Scholar 

  • Daniel, M., Wong, W.: A Phrase-Based, Joint Probability Model for Statistical Machine Translation.In: EMNLP (2002)

    Google Scholar 

  • Sonja, N., Ney, H.: Improving SMT quality with morpho-syntactic analysis. COLING 2000: The 18th International Conference on Computational Linguistics,pp. 1081-1085 (2000)

    Google Scholar 

  • Sonja, N., Ney, H.: Statistical Machine Translation with Scarce Resources Using Morpho-syntactic Information. Computational Linguistics 30(2), 181–204 (2004)

    CrossRef  Google Scholar 

  • Och, F., Ney, H.: Improved statistical alignment models. In: Proceedings of the 38th Annual Meeting of the Association for Computational Linguistics ,pp.440-447 (2000)

    Google Scholar 

  • Och, F., Ney, H.: A systematic comparison of various statistical alignment models. Computational Linguistics 29(1), 19–52 (2003)

    CrossRef  Google Scholar 

  • Stephan, V., Ney, H., Tillman HMM-based, C.: word alignment in statistical translation.In: Proceedings of COLING 1996: The 16th International Conference on Computational Linguistics,pp. 836-841

    Google Scholar 

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© 2004 Springer-Verlag Berlin Heidelberg

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Corston-Oliver, S., Gamon, M. (2004). Normalizing German and English Inflectional Morphology to Improve Statistical Word Alignment. In: Frederking, R.E., Taylor, K.B. (eds) Machine Translation: From Real Users to Research. AMTA 2004. Lecture Notes in Computer Science(), vol 3265. Springer, Berlin, Heidelberg.

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23300-8

  • Online ISBN: 978-3-540-30194-3

  • eBook Packages: Springer Book Archive