Abstract not found

The problems of access -- retrieving linguistic structure from some mental grammar -- and disambiguation -- choosing among these structures to correctly parse ambiguous linguistic input -- are fundamental to language understanding. The literature abounds with psychological results on lexical access, the access of idioms, syntactic rule access, parsing preferences, syntactic disambiguation, and the processing of garden-path sentences. Unfortunately, it has been difficult to combine models which account for these results to build a general, uniform model of access and disambiguation at the lexical, idiomatic, and syntactic levels. For example psycholinguistic theories of lexical access and idiom access and parsing theories of syntactic rule access have almost no commonality in methodology or coverage of psycholinguistic data. This paper presents a single probabilistic algorithm which models both the access and disambiguation of linguistic knowledge. The algorithm is based on a parallel parser which ranks constructions for access, and interpretations for disambiguation, by their conditional probability. Low-ranked constructions and interpretations are pruned through beam-search; this pruning accounts, among other things, for the garden-path effect. I show that this motivated probabilistic treatment accounts for a wide variety of psycholinguistic results, arguing for a more uniform representation of linguistic knowledge and for the use of probabilisticallyenriched grammars and interpreters as models of human knowledge of and processing of language.

Abstract not found

Natural language is an easy and effective medium for describing visual ideas and mental images. Thus, we foresee the emergence of language-based 3D scene generation systems to let ordinary users quickly create 3D scenes without having to learn special software, acquire artistic skills, or even touch a desktop window-oriented interface. WordsEye is such a system for automatically converting text into representative 3D scenes. WordsEye relies on a large database of 3D models and poses to depict entities and actions. Every 3D model can have associated shape displacements, spatial tags, and functional properties to be used in the depiction process. We describe the linguistic analysis and depiction techniques used by WordsEye along with some general strategies by which more abstract concepts are made depictable.

We present Embodied Construction Grammar, a formalism for linguistic analysis designed specifically for integration into a simulation-based model of language understanding. As in other construction grammars, linguistic constructions serve to map between phonological forms and conceptual representations.

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...

This paper describes the results of an implemented computational model that cashes out the belief that metaphor interpretation is grounded in embodied primitives. The speci c task addressed is the interpretation of simple causal narratives in the domains of Politics and Economics. The stories are taken from newspaper articles in these domains. When presented with a preparsed version of these narratives as input, the system described is able to generate commonsense inferences consistent with the input.

Languages vary dramatically in their structuring of space. Despite this wide variation, however, the search for universals in spatial semantics is well motivated by the fact that all linguistic spatial systems are based on human experience of space, which is in turn constrained by the nature of the human perceptual system. I present a connectionist model which contributes to the search for universals in this domain. Its design incorporates a number of structural devices motivated by neurobiological and psychophysical evidence concerning the human visual system; these provide a universal perceptual core which constrains the process of semantic acquisition. Using these structures, the model learns the perceptually grounded semantics for closed-class spatial terms from a range of languages --- providing at least a preliminary model of the human capacity for categorizing spatial events and relations. The model gives rise to two predictions concerning the manner in which one can e...

I describe an implemented computational model of verbal aspect that supports the proposition that the semantics of aspect is grounded in sensory-motor primitives. In this theory, aspectual expressions refer to schematized processes that recur in sensory-motor control (such as goal, periodicity, iteration, nal state, duration, and parameters such as force and e ort). This active model of aspect grounded in sensory-motor primitives is able to model cross-linguistic variation in aspectual expressions while avoiding some paradoxes and problems in model-theoretic and other traditional accounts.

This is Appendix A to the thesis " Learning to See Analogies: a Connectionist Exploration."