Resolving Part-of-Speech Ambiguity in the Greek Language Using Learning Techniques

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This article investigates the use of Transformation-Based Error-Driven learning for resolving part-of-speech ambiguity in the Greek language. The aim is not only to study the performance, but also to examine its dependence on different thematic domains. Results are presented here for two different test cases: a corpus on "management succession events" and a general-theme corpus. The two experiments show that the performance of this method does not depend on the thematic domain of the corpus, and its accuracy for the Greek language is around 95%.
6 pages. To appear in the Proceedings of the ECCAI Advanced Course on Artificial Intelligence(ACAI'99), Chania, Greece, July 1999

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Computation and Language, Artificial Intelligence, I.2.6 ; I.2.7

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http://salesiana.dossiersoluciones.com/handle/123456789/33219

Consulte el texto completo en el siguiente enlace:

https://arxiv.org/abs/cs/9906019
 http://arxiv.org/abs/cs/9906019
 In Fakotakis, N. et al. (Eds.), Machine Learning in Human Language Technology (Proceedings of the ACAI Workshop), pp. 29-34, Chania, Greece, 1999.

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arXiv