The Acquisition of Lexical Semantics for Spatial Terms: A Connectionist Model of Perceptual Categorization

TitleThe Acquisition of Lexical Semantics for Spatial Terms: A Connectionist Model of Perceptual Categorization
Publication TypeTechnical Report
Year of Publication1992
AuthorsRegier, T.
Other Numbers767
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. The design and construction of this system have resulted in several technical contributions. The first is a very simple but effective means of learning without explicit negative evidence. This thesis also presents the notion of partially-structured connectionism, a marriage of structured and unstructured network design techniques capturing the best of each paradigm. Finally, the idea of learning within highly specialized structural devices is introduced. Scientifically, the primary result of the work described here is a computational model of the acquisition of visually grounded semantics. This model successfully learns terms for spatial events and relations from a range of languages with widely differing spatial systems, including English, Mixtec (a Mexican Indian language), German, Bengali, and Russian. And perhaps most importantly, the model does more than just recapitulate the data; it also generates a number of falsifiable linguistic predictions regarding the sorts of semantic features, and combinations of features, one might expect to find in lexemes for spatial events and relations in the world's natural languages.

URLhttp://www.icsi.berkeley.edu/ftp/global/pub/techreports/1992/tr-92-062.pdf
Bibliographic Notes

ICSI Technical Report TR-92-062

Abbreviated Authors

T. Regier

ICSI Publication Type

Technical Report