Exploring automatic word sense disambiguation with decision lists and the Web
<|reference_start|>Exploring automatic word sense disambiguation with decision lists and the Web: The most effective paradigm for word sense disambiguation, supervised learning, seems to be stuck because of the knowledge acquisition bottleneck. In this paper we take an in-depth study of the performance of decision lists on two publicly available corpora and an additional corpus automatically acquired from the Web, using the fine-grained highly polysemous senses in WordNet. Decision lists are shown a versatile state-of-the-art technique. The experiments reveal, among other facts, that SemCor can be an acceptable (0.7 precision for polysemous words) starting point for an all-words system. The results on the DSO corpus show that for some highly polysemous words 0.7 precision seems to be the current state-of-the-art limit. On the other hand, independently constructed hand-tagged corpora are not mutually useful, and a corpus automatically acquired from the Web is shown to fail.<|reference_end|>
@article{agirre2000exploring,
    title={Exploring automatic word sense disambiguation with decision lists and
  the Web},
    author={Eneko Agirre and David Martinez},
    journal={Procedings of the COLING 2000 Workshop on Semantic Annotation and
  Intelligent Content},
    year={2000},
    archivePrefix={arXiv},
    eprint={cs/0010024},
    primaryClass={cs.CL}
}