Using a Probabilistic Class-Based Lexicon for Lexical Ambiguity Resolution
<|reference_start|>Using a Probabilistic Class-Based Lexicon for Lexical Ambiguity Resolution: 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.<|reference_end|>
@article{prescher2000using,
    title={Using a Probabilistic Class-Based Lexicon for Lexical Ambiguity
  Resolution},
    author={Detlef Prescher, Stefan Riezler, Mats Rooth},
    journal={Proceedings of the 18th COLING, 2000},
    year={2000},
    archivePrefix={arXiv},
    eprint={cs/0008035},
    primaryClass={cs.CL}
}