Modeling the acquisition of quantifier semantics: a case study in function word learnability

This paper studies the acquisition of quantifier meanings as a case study of function word learnability. We suggest that learners construct semantic representations of quantifiers and other function words using a compositional statistical learning mechanism that operates over a small set of domaingeneral cognitive primitives. We present a simple cross-situational learning model that provably solves key learning problems in this domain, using a developmentally-plausible amount of data. We additionally evaluate the utility of proposed constraints on quantifier meaning, and show that learning in an unrestricted space of meanings is not substantially more difficult than learning in highly-constrained frameworks.