This paper describes an experimental compari- son 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 tech- niques. The specific problem tested involves dis- ambiguating 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 prob- lem and we discuss a potential reason for this ob- served difference. We also discuss the role of bias in machine learning and its importance in explain- ing performance differences observed on specific problems.

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