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- W2590280377 abstract "Formal semantic theories are designed to explain how it is possible to produce and understand an infinite number of sentences on the basis of a finite lexicon and a finite number of composition rules. According to this architecture, language comprehension completely proceeds in a bottom-up fashion only driven by linear linguistic input thereby leaving no room for a predictive component which allows to make expectations about upcoming words. This is in stark contrast to neurophysiological research in the past decades on online semantic processing which has provided ample evidence that prediction plays indeed an indispensable role in language comprehension the brain as a prediction machine, [Ber10]. In this article, we present an extension of formal semantic theory that allows to make predictions of upcoming words. The basic intuition is: predictions are based on incomplete information. Drawing defeasible conclusions based on such information can be modeled by default reasoning. Since predictions can go wrong, a second strategy for retracting wrong guesses is needed in order to integrate unexpected words into the prior context. This is modeled by belief revision. We model both processing stages, making predictions about upcoming words and integrating them into the prior context, and relate the models to the empirical findings in neurophysiological research." @default.
- W2590280377 created "2017-03-03" @default.
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- W2590280377 date "2017-01-01" @default.
- W2590280377 modified "2023-09-26" @default.
- W2590280377 title "Semantic Predictions in Natural Language Processing, Default Reasoning and Belief Revision" @default.
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- W2590280377 doi "https://doi.org/10.1007/978-3-662-54332-0_8" @default.
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