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Using a Probabilistic Class-Based Lexicon for Lexical Ambiguity Resolution

Detlef Prescher·Stefan Riezler·Mats Rooth
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Abstract

This 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.

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Detlef Prescher, Stefan Riezler, Mats Rooth. 2000-08-30. Using a Probabilistic Class-Based Lexicon for Lexical Ambiguity Resolution. https://arxiv.org/abs/cs/0008035

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