A Probabilistic Model of Semantic Plausibility in Sentence Processing  

Oleh:

Pado, Ulrike ; Keller, Frank ; Crocker, Mathew W

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: Cognitive Science vol. 33 no. 5 (Jul. 2009), page 794–838.
Topik: Linguistics; Psychology; Language understanding; Semantics; Syntax; Symbolic computational modeling; Sentence processing; Probabilistic modeling; Probabilistic parsing; Thematic roles
Fulltext: 05. A Probabilistic Model of Semantic Plausibility in Sentence Processing.pdf (506.87KB)

Isi artikel

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.

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