ABSTRACT
Given a small number of examples of scene-utterance pairs of a novel verb, language learners can learn its syntactic and semantic features. Syntactic and semantic bootstrapping hypotheses both rely on cross-situational observation to hone in on the ambiguity present in a single observation. In this paper, we cast the distributional evidence from scenes and syntax in a unified Bayesian probablistic framework. Unlike previous approaches to modeling lexical acquisition, our framework uniquely: (1) models learning from only a small number of scene-utterance pairs (2) utilizes and integrates both syntax and semantic evidence, thus reconciling the apparent tension between syntactic and semantic bootststrapping approaches (3) robustly handles noise (4) makes prior and acquired knowledge distinctions explicit, through specification of the hypothesis space, prior and likelihood probability distributions.
