A Comparison between Supervised Learning Algorithms for Word Sense Disambiguation
<|reference_start|>A Comparison between Supervised Learning Algorithms for Word Sense Disambiguation: This paper describes a set of comparative experiments, including cross-corpus evaluation, between five alternative algorithms for supervised Word Sense Disambiguation (WSD), namely Naive Bayes, Exemplar-based learning, SNoW, Decision Lists, and Boosting. Two main conclusions can be drawn: 1) The LazyBoosting algorithm outperforms the other four state-of-the-art algorithms in terms of accuracy and ability to tune to new domains; 2) The domain dependence of WSD systems seems very strong and suggests that some kind of adaptation or tuning is required for cross-corpus application.<|reference_end|>
@article{escudero2000a,
    title={A Comparison between Supervised Learning Algorithms for Word Sense
  Disambiguation},
    author={Gerard Escudero, Lluis Marquez, German Rigau},
    journal={Proceedings of the 4th Conference on Computational Natural
  Language Learning, CoNLL'2000, pp. 31-36},
    year={2000},
    archivePrefix={arXiv},
    eprint={cs/0009022},
    primaryClass={cs.CL cs.AI}
}