Abstract
Statistical translation models can be inferred from bilingual samples whenever enough training data are available. However, bilingual corpora are usually too scarce resources so as to get reliable statistical models, particularly, when we are dealing with very inflected languages, or with agglutinative languages, where many words appear just once. Such events often distort the statistics. In order to cope with this problem, we have turned to morphological knowledge. Instead of dealing directly with running words, we also take advantage of lemmas, thus, producing the translation in two stages. In the first stage we transform the source sentence into a lemmatized target sentence, and in the second stage we convert the lemmatized target sentence into the target full forms.
This work has been partially supported by the Industry Department of the Basque Government and by the University of the Basque Country under grants INTEK CN02AD02 and 9/UPV 00224.310-15900/2004 respectively.
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Pérez, A., Torres, I., Casacuberta, F. (2006). Towards the Improvement of Statistical Translation Models Using Linguistic Features. In: Salakoski, T., Ginter, F., Pyysalo, S., Pahikkala, T. (eds) Advances in Natural Language Processing. FinTAL 2006. Lecture Notes in Computer Science(), vol 4139. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11816508_71
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DOI: https://doi.org/10.1007/11816508_71
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