Abstract
We address the problem of morpho-syntactic disambiguation of arbitrary texts in a highly inflectional natural language. We use a large tagset (615 tags), EAGLES and MULTEXT compliant [5]. The large tagset is internally mapped onto a reduced one (82 tags), serving statistical disambiguation, and a text disambiguated in terms of this tagset is subsequently subject to a recovery process of all the information left out from the large tagset. This two step process is called tiered tagging. To further improve the tagging accuracy we use a combined language models classifier, a procedure that interpolates the results of tagging the same text with several register-specific language models.
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© 1999 Springer-Verlag Berlin Heidelberg
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Tufiş, D. (1999). Tiered Tagging and Combined Language Models Classifiers. In: Matousek, V., Mautner, P., Ocelíková, J., Sojka, P. (eds) Text, Speech and Dialogue. TSD 1999. Lecture Notes in Computer Science(), vol 1692. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48239-3_5
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DOI: https://doi.org/10.1007/3-540-48239-3_5
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