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AIC Seminar Series

Bayesian Models of Human Sentence Processing

Srinivas NarayananAritificial Intelligence Center, SRI International[Home Page]

Date:  2001-12-10 at 16:00

Location:  EJ228  (Directions)

   Abstract

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, Boulder.

   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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