Please use this identifier to cite or link to this item: doi:10.22028/D291-30981
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Title: Modeling Semantic Expectation: Using Script Knowledge for Referent Prediction
Author(s): Modi, Ashutosh
Titov, Ivan
Demberg, Vera
Sayeed, Asad
Pinkal, Manfred
Language: English
Title: Transactions of the Association for Computational Linguistics
Volume: 5
Startpage: 31
Endpage: 44
Publisher/Platform: ACL
Year of Publication: 2017
Publikation type: Journal Article
Abstract: Recent research in psycholinguistics has provided increasing evidence that humans predict upcoming content. Prediction also affects perception and might be a key to robustness in human language processing. In this paper, we investigate the factors that affect human prediction by building a computational model that can predict upcoming discourse referents based on linguistic knowledge alone vs. linguistic knowledge jointly with common-sense knowledge in the form of scripts. We find that script knowledge significantly improves model estimates of human predictions. In a second study, we test the highly controversial hypothesis that predictability influences referring expression type but do not find evidence for such an effect.
DOI of the first publication: 10.1162/tacl_a_00044
URL of the first publication:
Link to this record: hdl:20.500.11880/29700
ISSN: 2307-387X
Date of registration: 22-Sep-2020
Faculty: MI - Fakultät für Mathematik und Informatik
Department: MI - Informatik
Professorship: MI - Prof. Dr. Vera Demberg
Collections:SciDok - Der Wissenschaftsserver der Universität des Saarlandes

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