Abstract
We report on a first attempt to perform cross-language spoken document retrieval. Without prior monolingual speech retrieval experience we applied the same general approach we use for bilingual retrieval that is typified by the use of overlapping character n-grams for tokenization and a statistical language model of retrieval. An innovative approach was adopted for coping with out-of-vocabulary words and misspelled or mistranscribed words: direct translation of individual n-grams was the sole mechanism to translate source language queries into target language terms. Though this approach shows promise, especially for non-speech retrieval, our performance appears to lag that of other teams participating in this novel evaluation.
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References
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McNamee, P., Mayfield, J. (2004). N-Grams for Translation and Retrieval in CL-SDR. In: Peters, C., Gonzalo, J., Braschler, M., Kluck, M. (eds) Comparative Evaluation of Multilingual Information Access Systems. CLEF 2003. Lecture Notes in Computer Science, vol 3237. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30222-3_63
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DOI: https://doi.org/10.1007/978-3-540-30222-3_63
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24017-4
Online ISBN: 978-3-540-30222-3
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