Similarity-Based Methods For Word Sense Disambiguation
<|reference_start|>Similarity-Based Methods For Word Sense Disambiguation: We compare four similarity-based estimation methods against back-off and maximum-likelihood estimation methods on a pseudo-word sense disambiguation task in which we controlled for both unigram and bigram frequency. The similarity-based methods perform up to 40% better on this particular task. We also conclude that events that occur only once in the training set have major impact on similarity-based estimates.<|reference_end|>
@article{dagan1997similarity-based,
    title={Similarity-Based Methods For Word Sense Disambiguation},
    author={Ido Dagan (Bar-Ilan University), Lillian Lee (Cornell University), and
  Fernando Pereira (AT&T Labs -- Research)},
    journal={Proceedings of the 35th ACL/8th EACL, pp 56--63},
    year={1997},
    archivePrefix={arXiv},
    eprint={cmp-lg/9708010},
    primaryClass={cmp-lg cs.CL}
}