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 volume brings together distinguished scholars from all over the world to present an authoritative, thorough, and yet accessible state-of-the-art survey of current issues in pragmatics. Following an introduction by the editor, the volume is divided into five thematic parts. Chapters in Part I are concerned with schools of thought, foundations, and theories, while Part II deals with central topics in pragmatics, including implicature, presupposition, speech acts, deixis, reference, and context. In Part III, the focus is on cognitively-oriented pragmatics, covering topics such as computational, experimental, and neuropragmatics. Part IV takes a look at socially and culturally-oriented pragmatics such as politeness/impoliteness studies, cross- and intercultural, and interlanguage pragmatics. Finally, the chapters in Part V explore the interfaces of pragmatics with semantics, grammar, morphology, the lexicon, prosody, language change, and information structure. The Oxford Handbook of Pragmatics will be an indispensable reference for scholars and students of pragmatics of all theoretical stripes. It will also be a valuable resource for linguists in other fields, including philosophy of language, semantics, morphosyntax, prosody, psycholinguistics, and sociolinguistics, and for researchers and students in the fields of cognitive science, artificial intelligence, computer science, anthropology, and sociology.

This handbook presents an overview of the phenomenon of reference - the ability to refer to and pick out entities - which is an essential part of human language and cognition. In the volume's 21 chapters, international experts in the field offer a critical account of all aspects of reference from a range of theoretical perspectives. Chapters in the first part of the book are concerned with basic questions related to different types of referring expression and their interpretation. They address questions about the role of the speaker - including speaker intentions - and of the addressee, as well as the role played by the semantics of the linguistic forms themselves in establishing reference. This part also explores the nature of such concepts as definite and indefinite reference and specificity, and the conditions under which reference may fail. The second part of the volume looks at implications and applications, with chapters covering such topics as the acquisition of reference by children, the processing of reference both in the human brain and by machines. The volume will be of interest to linguists in a wide range of subfields, including semantics, pragmatics, computational linguistics, and psycho- and neurolinguistics, as well as scholars in related fields such as philosophy and computer science.

The volume Questions in Discourse - Vol. 1 Semantics contains a comprehensive overview of the semantic analysis of questions and their role in structuring discourse, next to a series of in-depth contributions on individual aspects of question meanings. The expert contributions offer novel accounts of semantic phenomena such as negation and biased questions, question embedding, exhaustivity, disjunction in alternative questions, and superlative quantification particles in questions. Some accounts are modelled in the framework of inquisitive semantics, whereas others employ alternative semantics, and yet others point to the discourse-structuring potential of marked questions. All contributions are easily accessible against the background of the general introduction. Together, they give an excellent overview of current trends in question semantics.

Covering a wide range of fields and theoretical perspectives, this book provides a novel philosophical account of theoretical linguistics.

This book presents a new theory of the relationship between vagueness, context-sensitivity, gradability, and scale structure in natural language. Heather Burnett proposes a new formal reasoning system called DelTCS in which she sets out a completely new theory of gradable linguistic constructions.

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

All of the papers included in this volume offer some novel and/or updated perspective on issues of central importance in pragmatics, suggesting original ways in which research in the particular areas they adhere to could advance. Apart from the obvious aim of motivating further discussion on the topics it touches on, a central objective of this volume is to underline that research in pragmatics can and does substantially inform research in numerous other fields of enquiry, namely philosophy, cognitive science, linguistics and conversation analysis, revealing in this way the truly interdisciplinary nature of pragmatics theorizing. In this respect, and given that most of the contributions in this volume are from leading scholars in their respective fields, it is clearly expected that the ideas put forth in this volume will have a profound and long-lasting impact for future research in the area.

Labelling data is one of the most fundamental activities in science, and has underpinned practice, particularly in medicine, for decades, as well as research in corpus linguistics since at least the development of the Brown corpus. With the shift towards Machine Learning in Artificial Intelligence (AI), the creation of datasets to be used for training and evaluating AI systems, also known in AI as corpora, has become a central activity in the field as well. Early AI datasets were created on an ad-hoc basis to tackle specific problems. As larger and more reusable datasets were created, requiring greater investment, the need for a more systematic approach to dataset creation arose to ensure increased quality. A range of statistical methods were adopted, often but not exclusively from the medical sciences, to ensure that the labels used were not subjective, or to choose among different labels provided by the coders. A wide variety of such methods is now in regular use. This book is meant to provide a survey of the most widely used among these statistical methods supporting annotation practice. As far as the authors know, this is the first book attempting to cover the two families of methods in wider use. The first family of methods is concerned with the development of labelling schemes and, in particular, ensuring that such schemes are such that sufficient agreement can be observed among the coders. The second family includes methods developed to analyze the output of coders once the scheme has been agreed upon, particularly although not exclusively to identify the most likely label for an item among those provided by the coders. The focus of this book is primarily on Natural Language Processing, the area of AI devoted to the development of models of language interpretation and production, but many if not most of the methods discussed here are also applicable to other areas of AI, or indeed, to other areas of Data Science.