Proceedings of the Annual Meeting of the Cognitive Science Society

This paper describes the mechanism used by the A L A C K language acquisition program for identification of auxiliary verbs. Pinker's approach to this problem (Pinker, 1984) is a general learning algorithm that can learn any Boolean function but takes time exponential in the number of feature dimensions. In this paper, we describe an approach that improves upon Pinker's method by introducting the Closed-Category Relevance Property, and showing how it provides the basis of an algorithm that learns the cleiss of Boolean functions that is believed suffcient for natural language, and does not require more than linear time as feature dimensions are added.