A Probabilistic Model of Semantic Plausibility in Sentence Processing

Ulrike Padó, Matthew W. Crocker, Frank Keller

Research output: Contribution to journalArticlepeer-review


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 widecoverage
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.
Original languageEnglish
Pages (from-to)1-43
Number of pages43
JournalCognitive Science
Issue number5
Publication statusPublished - 2009

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