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Probabilistic Modeling of Discourse‐Aware Sentence Processing
Author(s) -
Dubey Amit,
Keller Frank,
Sturt Patrick
Publication year - 2013
Publication title -
topics in cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.191
H-Index - 56
eISSN - 1756-8765
pISSN - 1756-8757
DOI - 10.1111/tops.12023
Subject(s) - sentence processing , natural language processing , computer science , probabilistic logic , sentence , artificial intelligence , linguistics , psychology , speech recognition , philosophy
Probabilistic models of sentence comprehension are increasingly relevant to questions concerning human language processing. However, such models are often limited to syntactic factors. This restriction is unrealistic in light of experimental results suggesting interactions between syntax and other forms of linguistic information in human sentence processing. To address this limitation, this article introduces two sentence processing models that augment a syntactic component with information about discourse co‐reference. The novel combination of probabilistic syntactic components with co‐reference classifiers permits them to more closely mimic human behavior than existing models. The first model uses a deep model of linguistics, based in part on probabilistic logic, allowing it to make qualitative predictions on experimental data; the second model uses shallow processing to make quantitative predictions on a broad‐coverage reading‐time corpus.