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

Simple item page
dc.creatorPetasis, G.
dc.creatorPaliouras, G.
dc.creatorKarkaletsis, V.
dc.creatorSpyropoulos, C. D.
dc.creatorAndroutsopoulos, I.
dc.date1999-06-22
dc.date1999-06-30
dc.date.accessioned2026-07-07T03:24:09Z
dc.date.available2026-07-07T03:24:09Z
dc.descriptionThis 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%.
dc.description6 pages. To appear in the Proceedings of the ECCAI Advanced Course on Artificial Intelligence(ACAI'99), Chania, Greece, July 1999
dc.identifierhttps://arxiv.org/abs/cs/9906019
dc.identifierhttp://arxiv.org/abs/cs/9906019
dc.identifierIn Fakotakis, N. et al. (Eds.), Machine Learning in Human Language Technology (Proceedings of the ACAI Workshop), pp. 29-34, Chania, Greece, 1999.
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/33219
dc.subjectComputation and Language
dc.subjectArtificial Intelligence
dc.subjectI.2.6 ; I.2.7
dc.titleResolving Part-of-Speech Ambiguity in the Greek Language Using Learning Techniques
dc.typetext

Files

Collections

arXiv