A Probabilistic Model of Early Argument Structure Acquisition by Afra Alishahi A thesis submitted in conformity with the requirements

A Probabilistic Model of Early Argument Structure Acquisition Afra Alishahi Doctor of Philosophy Graduate Department of Computer Science University of Toronto 2008 Developing computational algorithms that capture the complex structure of natural language is an open problem. In particular, learning the abstract properties of language only from usage data remains a challenge. In this dissertation, we present a probabilistic usage-based model of verb argument structure acquisition that can successfully learn abstract knowledge of language from instances of verb usage, and use this knowledge in various language tasks. The model demonstrates the feasibility of a usage-based account of language learning, and provides concrete explanation for the observed patterns in child language acquisition. We propose a novel representation for the general constructions of language as probabilistic associations between syntactic and semantic features of a verb usage; these associations generalize over the syntactic patterns and the fine-grained semantics of both the verb and its arguments. The probabilistic nature of argument structure constructions in the model enables it to capture both statistical effects in language learning, and adaptability in language use. The acquisition of constructions is modeled as detecting similar usages and grouping them together. We use a probabilistic measure of similarity between verb usages, and a Bayesian framework for clustering them. Language use, on the other hand, is modeled as a prediction problem: each language task is viewed as finding the best value for a missing feature in a usage, based on the available features in that same