TR available

[Terry Regier]

The technical report version of my dissertation is now available 
by ftp.  Ftp instructions follow the abstract.


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

			Terry Regier
			UC Berkeley

		ICSI technical report TR-92-062

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 words 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 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 thesis introduces
the idea of learning within highly specialized structural devices.

Scientifically, the primary result of the work described here is a 
computational model of the acquisition of visually grounded semantics.
This model successfully learns words 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.


To ftp:

	% ftp icsi-ftp.berkeley.edu
	Name:  anonymous
	Password:  [your e-mail address]
	ftp> binary
	ftp> cd pub/techreports
	ftp> get tr-92-062.ps.Z
	ftp> quit
	% uncompress tr-92-062.ps.Z
	% lpr -P[your-postscript-printer] tr-92-062.ps

-- 

 ----------------------------------------------------------------------   
 Terry Regier     
 Computer Science, UC Berkeley                 regier at icsi.Berkeley.EDU   
 International Computer Science Institute