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.

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In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP-96), pp. 82-91, Philadelphia, PA 1996.

@InProceedings{mooney:emnlp96, title={Comparative Experiments on Disambiguating Word Senses: An Illustration of the Role of Bias in Machine Learning}, author={Raymond J. Mooney}, booktitle={Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP-96)}, address={Philadelphia, PA}, pages={82-91}, url="http://www.cs.utexas.edu/users/ai-lab?mooney:emnlp96", year={1996} }