Henk Zeevat – författare

This open access book presents novel theoretical, empirical and experimental work exploring the nature of mental representations that support natural language production and understanding, and other manifestations of cognition.

The 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.

The 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.

The 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.

This book constitutes the refereed proceedings of the 11th International Tbilisi Symposium on Logic, Language and Computation, TbiLLC 2015, held in Tbilisi, Georgia, in September 2015.The 18 papers in this book were selected from the invited submissions of full, revised versions of the 37 short papers presented at the conference, and one invited talk. Each paper has passed through a rigorous peer-review process before being accepted for publication.The biennial conference series and the proceedings are representative of the aims of the organizing institutes: to promote the integrated study of logic, information and language. The scientific program consisted of tutorials, invited lectures, contributed talks, and two workshops.

This book constitutes the refereed proceedings of the 11th International Tbilisi Symposium on Logic, Language and Computation, TbiLLC 2015, held in Tbilisi, Georgia, in September 2015.

The 18 papers in this book were selected from the invited submissions of full, revised versions of the 37 short papers presented at the conference, and one invited talk. Each paper has passed through a rigorous peer-review process before being accepted for publication.

The biennial conference series and the proceedings are representative of the aims of the organizing institutes: to promote the integrated study of logic, information and language. The scientific program consisted of tutorials, invited lectures, contributed talks, and two workshops.

An utterance is normally produced by a speaker in linear time and the hearer normally correctly identifies the speaker intention in linear time and incrementally. This is hard to understand in a standard competence grammar since languages are highly ambiguous and context-free parsing is not linear. Deterministic utterance generation from intention and n-best Bayesian interpretation, based on the production grammar and the prior probabilities that need to be assumed for other perception do much better. The proposed model uses symbolic grammar and derives symbolic semantic representations, but treats interpretation as just another form of perception. Removing interpretation from grammar is not only empirically motivated, but also makes linguistics a much more feasible enterprise. The importance of Henk Zeevat's new monograph cannot be overstated. Its combination of breadth, formal rigor, and originality is unparalleled in work on the form-meaning interface in human language...Zeevat's is the first proposal which provides a computationally feasible integrated treatment of production and comprehension for pragmatics, semantics, syntax, and even phonology. I recommend it to anyone who combines interests in language, logic, and computation with a sense of adventure. David Beaver, University of Texas at Austin