This paper presents the result that a computer program can mimic the acquisition by children of a sdected set of grammatical morphemes. Roger Brown [Brown, 1973] studied the acquisition of 14 morphemes, and showed how a set of partial order relations describes this aspect of child language learning. W e show that these relations can be given a computational basis. They follow directly from a class of Boolean learning algorithms which have three simple constraints in the manner in which they consider hypotheses. I will call these three constraints the C A M constraints. C A M constraint 1 is to increase the length of the conjuncts one term at a time. The second C A M constraint is to consider all hypotheses of the same length simultaneously. Finally, C A M constraint 3 is to collect all single-term hypotheses involving noun features into a single conjunction prior to Boolean learning.

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.