Explanation-Based Generalization: A Unifying View
Machine Learning
Explanation-Based Learning: An Alternative View
Machine Learning
A process model of language acquisition
A process model of language acquisition
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Modeling learning in any domain can be pursued in two alternative directions: either by finding domain-specific heuristics, or by applying general machine-learning methods. In the linguistic domain, programs such as FOULUP [1] and CHILD [2] have used specific heuristics, regardless of the general learning methodology; other programs [3,4] have focussed on the general learning methodology. In our particular task-learning idioms from examples, the evaluation of general learning models is very appealing, since such methods have been studied extensively, and may offer ready solutions. Language is a special domain in regard to learning. Consider for example behavior of idioms. By definition, idiosyncratic properties are not systematic and are not predictable. Thus, how do people learn such properties from examples? What is the general learning model, therefore, which accounts for idiom acquisition? We examine here the processes involved in acquisition of idioms, and relate them to existing machine-learning paradigms.