Abstract
This paper describes a new technique for the direct translation of character n-grams for use in Cross-Language Information Retrieval systems. This solution avoids the need for word normalization during indexing or translation, and it can also deal with out-of-vocabulary words. This knowledge-light approach does not rely on language-specific processing, and it can be used with languages of very different natures even when linguistic information and resources are scarce or unavailable. Our proposal also tries to achieve a higher speed during the n-gram alignment process with respect to previous approaches.
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References
Amati, G., van Rijsbergen, C.J.: Probabilistic models of Information Retrieval based on measuring divergence from randomness. ACM Transactions on Information Systems 20(4), 357–389 (2002)
Koehn, P.: Europarl: A Parallel Corpus for Statistical Machine Translation. In: Proc. of the 10th Machine Translation Summit, pp. 79–86 (2005), Corpus available in http://www.iccs.inf.ed.ac.uk/~pkoehn/publications/europarl/
Koehn, P., Och, F.J., Marcu, D.: Statistical phrase-based translation. In: Proc. of the 2003 Conf. of the North American Chapter of the ACL, pp. 48–54 (2003)
Manning, C.D., Schütze, H.: Foundations of Statistical Natural Language Processing. The MIT Press, Cambridge (1999)
McNamee, P., Mayfield, J.: Character N-gram Tokenization for European Language Text Retrieval. Information Retrieval 7(1-2), 73–97 (2004)
McNamee, P., Mayfield, J.: JHU/APL experiments in tokenization and non-word translation. In: LNCS, vol. 3237, pp. 85–97. Springer, Heidelberg (2004)
Nardi, A., Peters, C., Vicedo, J.L. (eds.): Working Notes of the CLEF 2006, Workshop (2006), available at http://www.clef-campaign.org
Och, F.J., Ney, H.: A systematic comparison of various statistical alignment models, Source code (2003), available at http://www.fjoch.com/GIZA++.html
Porter, M.F.: An algorithm for suffix stripping. Program 14(3), 130–137 (1980)
J. Savoy. Cross-Language Information Retrieval: experiments based on CLEF 2000 corpora. Information Processing and Management 39, 75–115 (2003)
Vilares, J., Oakes, M.P., Tait, J.I.: CoLesIR at CLEF 2000 rapid prototyping of a N-gram-based CLIR system. In: Nardi, et al. (2006)
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Vilares, J., Oakes, M.P., Vilares, M. (2007). Character N-Grams Translation in Cross-Language Information Retrieval. In: Kedad, Z., Lammari, N., Métais, E., Meziane, F., Rezgui, Y. (eds) Natural Language Processing and Information Systems. NLDB 2007. Lecture Notes in Computer Science, vol 4592. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73351-5_19
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DOI: https://doi.org/10.1007/978-3-540-73351-5_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73350-8
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