Using a Probabilistic Class-Based Lexicon for Lexical Ambiguity Resolution

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dc.creatorPrescher, Detlef
dc.creatorRiezler, Stefan
dc.creatorRooth, Mats
dc.date2000-08-30
dc.date.accessioned2026-07-07T03:16:31Z
dc.date.available2026-07-07T03:16:31Z
dc.descriptionThis paper presents the use of probabilistic class-based lexica for disambiguation in target-word selection. Our method employs minimal but precise contextual information for disambiguation. That is, only information provided by the target-verb, enriched by the condensed information of a probabilistic class-based lexicon, is used. Induction of classes and fine-tuning to verbal arguments is done in an unsupervised manner by EM-based clustering techniques. The method shows promising results in an evaluation on real-world translations.
dc.description7 pages, uses colacl.sty
dc.identifierhttps://arxiv.org/abs/cs/0008035
dc.identifierhttp://arxiv.org/abs/cs/0008035
dc.identifierProceedings of the 18th COLING, 2000
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/30382
dc.subjectComputation and Language
dc.subjectI.2.6, I.2.7
dc.titleUsing a Probabilistic Class-Based Lexicon for Lexical Ambiguity Resolution
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