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|Title:||Modeling Semantic Expectation: Using Script Knowledge for Referent Prediction|
|Title:||Transactions of the Association for Computational Linguistics|
|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:||https://www.aclweb.org/anthology/Q17-1003/|
|Link to this record:||hdl:20.500.11880/29700|
|Date of registration:||22-Sep-2020|
|Faculty:||MI - Fakultät für Mathematik und Informatik|
|Department:||MI - Informatik|
|Professorship:||MI - Prof. Dr. Vera Demberg|
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