Quantitative evaluation of explanation-based learning as an optimization tool for a large-scale natural language system

Abstract

This paper describes the application of explanationbased learning, a machine learning technique, to the SRI Core Language Engine, a large scale general purpose natural language analysis system. The idea is to bypass normal morphological, syntactic and (partly) semantic processing, for most input sentences, instead using a set of learned rules. Explanation-based learning is used to extract the learned rules automatically from sample sentences submitted by a user and thus tune the system for that particular user. By indexing the learned rules efficiently, it is possible to achieve dramatic speedups. Performance measurements were carried out using a training set of 1500 sentences and a separate