Abstract
Human language processing relies on many kinds of linguistic
knowledge, and is sensitive to their frequency, including lexical frequencies (Tyler, 1984; Salasoo & Pisoni, 1985; Marslen-Wilson, 1990; Zwitserlood, 1989; Simpson & Burgess, 1985),
idiom frequencies (d'Arcais, 1993), phonological neighborhood frequencies (Luce, Pisoni, & Goldfinger, 1990), subcategorization frequencies (Trueswell, Tanenhaus, & Kello, 1993),
and thematic role frequencies (Trueswell, Tanenhaus, & Garnsey, 1994; Garnsey, Pearlmutter, Myers, & Lotocky, 1997).
But while we know that each of these knowledge sources must
be probabilistic, we know very little about exactly how these
probabilistic knowledge sources are combined. This paper proposes the use of Bayesian decision trees in modeling 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. We show that our Bayesian approach successfully models psycholinguistic results on evidence combination in human
lexical, idiomatic, and syntactic/semantic processing.
Description
A Bayesian Model Predicts Human Parse Preference and Reading Times in Sentence Processing - Narayanan, Jurafsky (ResearchIndex)
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