Vectorial representations of meaning for a computational model of language comprehension.

摘要

This thesis aims to define and extend a line of computational models for text comprehension that are humanly plausible. Since natural language is human by nature, computational models of human language will always be just that --- models. To the degree that they miss out on information that humans would tap into, they may be improved by considering the human process of language processing in a linguistic, psychological, and cognitive light.;Approaches to constructing vectorial semantic spaces often begin with the distributional hypothesis, i.e., that words can be judged 'by the company they keep.' Typically, words that occur in the same documents are similar, and will have similar vectorial meaning representations. However, this does not in itself provide a way for two distinct meanings to be composed, and it ignores syntactic context.;Both of these problems are solved in Structured Vectorial Semantics (SVS), a new framework that fully unifies vectorial semantics with syntactic parsing. Most approaches that try to combine syntactic and semantic information will either lack a cohesive semantic component or a full-fledged parser, but SVS integrates both. Thus, in the SVS framework, interpretation is interactive, considering both syntax and semantics simultaneously.;Cognitively-plausible language models would also be incremental, support linear-time inference, and operate in only a bounded store of short-term memory. Each of these characteristics is supported by right-corner Hierarchical Hidden Markov Model (HHMM) parsing; therefore, SVS will be transformed into right-corner form and mapped to an HHMM parser. The resulting representation will then encode a psycholinguistically plausible incremental SVS language model.