AIC Seminar Series
Bayesian Models of Human Sentence Processing
|Srinivas Narayanan||Aritificial Intelligence Center, SRI International||[Home Page]|
Date: 2001-12-10 at 16:00
Location: EJ228 (Directions)
Human language processing relies on many kinds of linguistic knowledge,
and is sensitive to their frequency, including lexical frequencies,
idiom frequencies, phonological neighborhood frequencies,
sub-categorization frequencies, and thematic role frequencies. But
while it is known that each of these knowledge sources must be
probabilistic, very little is known about exactly how these
probabilistic knowledge sources are combined. In earlier work, we
proposed the use of Graphical Models to model the probabilistic,
evidential nature of human sentence processing. Our method reifies
conditional independence assertions implicit in sign-based linguistic
theories and describes interactions among features without requiring
additional assumptions about modularity. This talk will report on
results that suggest that our Bayesian approach is able to model
psycholinguistic results on evidence combination in human lexical,
idiomatic, and syntactic/semantic processing. I will also report on more
recent work which tests the model's predictions against reading time
data from a psycholinguistic experiment.
The talk will be self contained and will not assume any background in
linguistics or human language processing research.
This is joint work with Daniel Jurafsky from the University of Colorado,
Bio for Srinivas Narayanan||
Srini Narayanan joined the AI Center in 1999 and works in the Natural
Language Group. Before joining SRI, he was a graduate student and then a
post-doctoral fellow in the Computer Science Division of the
University of California, Berkeley and the International Computer
Science Institute (ICSI) , Berkeley. There he worked with Jerry Feldman
and George Lakoff in the Neural Theory of Language group.
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