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100   $a20150619d2015      u|         0
101 0 $aeng
135   $aur|n|---|||||
181   $ctxt
182   $cc
183   $acr
200 10$aBayesian Natural Language Semantics and Pragmatics /$fedited by Henk Zeevat, Hans-Christian Schmitz
205   $a1st ed. 2015.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2015.
215   $a1 online resource (256 p.)
225 1 $aLanguage, Cognition, and Mind,$x2364-4109 ;$v2
300   $aDescription based upon print version of record.
311   $a3-319-17063-5 
320   $aIncludes bibliographical references at the end of each chapters.
327   $aPreface by Henk Zeevat & Hans-Christian Schmitz -- 1. Perspectives on Bayesian Natural Language Semantics and Pragmatics by Henk Zeevat -- 2. Causal Bayesian Networks, Signalling Games and Implicature of `More than n' by Anton Benz -- 3. Measurement-Theoretic Foundations of Logic for Better Questions and Answers by Satoru Suzuki -- 4. Conditionals, Conditional Probabilities, and Conditionalization by Stefan Kaufmann -- 5. On the Probabilistic Notion of Causality: Models and Metalanguages by Christian Wurm -- 6. Shannon vs. Chomsky: Brain Potentials and the Syntax-Semantics Distinction by Mathias Winther Madsen -- 7. Orthogonality and Presuppositions. A Bayesian Perspective by Jacques Jayez -- 8. Layered Meanings and Bayesian Argumentation: The Case of Exclusives by Grégoire Winterstein -- 9. Variations on a Bayesian Theme: Comparing Bayesian Models of Referential Reasoning by Ciyang Qing and Michael Franke -- 10. Towards a Probabilistic Semantics for Vague Adjectives by Peter Sutton.
330   $aThe contributions in this volume focus on the Bayesian interpretation of natural languages, which is widely used in areas of artificial intelligence, cognitive science, and computational linguistics. This is the first volume to take up topics in Bayesian Natural Language Interpretation and make proposals based on information theory, probability theory, and related fields. The methodologies offered here extend to the target semantic and pragmatic analyses of computational natural language interpretation.   Bayesian approaches to natural language semantics and pragmatics are based on methods from signal processing and the causal Bayesian models pioneered by especially Pearl. In signal processing, the Bayesian method finds the most probable interpretation by finding the one that maximizes the product of the prior probability and the likelihood of the interpretation. It thus stresses the importance of a production model for interpretation as in Grice?s contributions to pragmatics or in interpretation by abduction.
410  0$aLanguage, Cognition, and Mind,$x2364-4109 ;$v2
606   $aSemantics
606   $aComputational linguistics
606   $aNatural language processing (Computer science)
606   $aApplication software
606   $aSemantics$3https://scigraph.springernature.com/ontologies/product-market-codes/N39000
606   $aComputational Linguistics$3https://scigraph.springernature.com/ontologies/product-market-codes/N22000
606   $aNatural Language Processing (NLP)$3https://scigraph.springernature.com/ontologies/product-market-codes/I21040
606   $aComputer Appl. in Arts and Humanities$3https://scigraph.springernature.com/ontologies/product-market-codes/I23036
615  0$aSemantics.
615  0$aComputational linguistics.
615  0$aNatural language processing (Computer science)
615  0$aApplication software.
615 14$aSemantics.
615 24$aComputational Linguistics.
615 24$aNatural Language Processing (NLP).
615 24$aComputer Appl. in Arts and Humanities.
676   $a519.542
702   $aZeevat$b Henk$4edt$4http://id.loc.gov/vocabulary/relators/edt
702   $aSchmitz$b Hans-Christian$4edt$4http://id.loc.gov/vocabulary/relators/edt
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910484524803321
996   $aBayesian Natural Language Semantics and Pragmatics$92844463
997   $aUNINA