A Probabilistic Model of Semantic Plausibility in Sentence Processing

Cognitive Science 33 (5):794-838 (2009)
  Copy   BIBTEX


Experimental research shows that human sentence processing uses information from different levels of linguistic analysis, for example, lexical and syntactic preferences as well as semantic plausibility. Existing computational models of human sentence processing, however, have focused primarily on lexico‐syntactic factors. Those models that do account for semantic plausibility effects lack a general model of human plausibility intuitions at the sentence level. Within a probabilistic framework, we propose a wide‐coverage model that both assigns thematic roles to verb–argument pairs and determines a preferred interpretation by evaluating the plausibility of the resulting (verb, role, argument) triples. The model is trained on a corpus of role‐annotated language data. We also present a transparent integration of the semantic model with an incremental probabilistic parser. We demonstrate that both the semantic plausibility model and the combined syntax/semantics model predict judgment and reading time data from the experimental literature.



    Upload a copy of this work     Papers currently archived: 89,654

External links

Setup an account with your affiliations in order to access resources via your University's proxy server

Through your library

Similar books and articles

Uncertainty About the Rest of the Sentence.John Hale - 2006 - Cognitive Science 30 (4):643-672.
Probabilistic Grammars and Languages.András Kornai - 2011 - Journal of Logic, Language and Information 20 (3):317-328.
Semantic processing for finite domains.Martha Stone Palmer - 1990 - New York: Cambridge University Press.
A Probabilistic Model of Melody Perception.David Temperley - 2008 - Cognitive Science 32 (2):418-444.


Added to PP

44 (#313,814)

6 months
4 (#312,422)

Historical graph of downloads
How can I increase my downloads?