The acquisition of lexical semantics for spatial terms: a connectionist model of perceptual categorization

This thesis describes a connectionist model which learns to perceive spatial events and relations in simple movies of 2-dimensional objects, so as to name the events and Relations as a speaker of a particular natural language would, and learns perceptually grounded semantics for natural language spatial terms.

Abstract

This thesis describes a connectionist model which learns to perceive spatial events and relations in simple movies of 2-dimensional objects, so as to name the events and relations as a speaker of a particular natural language would. Thus, the model learns perceptually grounded semantics for natural language spatial terms. Natural languages differ -- sometimes dramatically -- in the ways in which they structure space. The aim here has been to have the model be able to perform this learning task for terms from any natural language, and to have learning take place in the absence of explicit negative evidence, in order to rule out ad hoc solutions and to approximate the conditions under which children learn. The central focus of this thesis is a connectionist system which has succeeded in learning spatial terms from a number of different languages. The design and construction of this system have resulted in several technical contributions. The first is a very simple but effective means of ...