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Top Cogn Sci. 2013 Jul;5(3):425-51. doi: 10.1111/tops.12023. Epub 2013 Apr 24.

Probabilistic modeling of discourse-aware sentence processing.

Author information

  • 1Google Inc. amit@dubey.ca

Abstract

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.

Copyright © 2013 Cognitive Science Society, Inc.

KEYWORDS:

Co-reference resolution; Cognitive modeling; Discourse/syntax interactions; Markov logic; Sentence processing

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