My primary line of
research involves the building and study
of psychocomputational models of first
language acquisition. In other words, I
use computational techniques to model
the process by which children learn the
grammar of their native
(first) language.
Most of these models
consist of three core components:
the linguistic framework (the
grammar formalism(s))
the linguistic environment
(the utterances encountered by
the language learner )
the algorithm which the
learner employs to achieve the
final (correct) grammar
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One key research
question is:
Given a framework
and an algorithm, what properties of
the linguistic environment are
most/least conducive to efficient
learning?
An important focus is
the effect of cross-language ambiguity
on learning efficiency. Many sentence
forms (for example Subject Verb
Object) occur across many languages.
It is unclear how children, given the
set of ambiguous forms they are
exposed to, are so efficiently able to
determine the correct grammar that
generates their native language.
I have demonstrated that
for a variety of proposed learning
algorithms (within Chomsky's
principles and parameters framework)
there is a narrow range of linguistic
conditions that support efficient
learning and that these conditions are
quite different for different
algorithms. This leads to the (perhaps
not so surprising) conclusion that the
success of any
computer model of human language
acquisition must be measured against
the match between the abstract
computational/linguistic environment
the
model 'lives in' and the actual facts
that delineate true human grammars.
Towards this end, together
with Janet Fodor, CUNY, I have
led a research group CUNY-CoLAG that
has created a systematic artificial,
but linguistically compelling,
language domain of over 3,000 abstract
languages. CUNY-CoLAG
and available papers here.
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