Comparative Experiments on Disambiguating Word Senses: An Illustration of the Role of Bias in Machine Learning
<|reference_start|>Comparative Experiments on Disambiguating Word Senses: An Illustration of the Role of Bias in Machine Learning: This paper describes an experimental comparison of seven different learning algorithms on the problem of learning to disambiguate the meaning of a word from context. The algorithms tested include statistical, neural-network, decision-tree, rule-based, and case-based classification techniques. The specific problem tested involves disambiguating six senses of the word ``line'' using the words in the current and proceeding sentence as context. The statistical and neural-network methods perform the best on this particular problem and we discuss a potential reason for this observed difference. We also discuss the role of bias in machine learning and its importance in explaining performance differences observed on specific problems.<|reference_end|>
@article{mooney1996comparative,
    title={Comparative Experiments on Disambiguating Word Senses: An Illustration
  of the Role of Bias in Machine Learning},
    author={Raymond J. Mooney (University of Texas at Austin)},
    journal={arXiv preprint arXiv:cmp-lg/9612001},
    year={1996},
    archivePrefix={arXiv},
    eprint={cmp-lg/9612001},
    primaryClass={cmp-lg cs.CL}
}