"This book offers a unique perspective on meaning in language, broadening the scope of existing understanding of meaning by introducing a comprehensive and cohesive account of meaning which draws on a wide range of linguistic approaches"--

It is hard to find someone who doesn't have a pet peeve about language. The act of bemoaning the decline of language has become something of a cottage industry. High profile, self-appointed language police worry that new forms of popular media are contributing to sloppiness, imprecision, and a general disregard for the rules of grammar and speech. Within linguistics the term "prescriptivism" is used to refer to the judgements that people make about language based on the idea that some forms and uses of language are correct and others incorrect. This book argues that prescriptivism is unfounded at its very core, and explores why it is, nevertheless, such a popular position. In doing so it addresses the politics of language: what prescriptivist positions about language use reveal about power, authority, and various social prejudices.

Invisible Language: Its Incalcuable Significance for Philosophy reveals that although the use of language is visible or audible, the medium employed boasts neither of these attributes. Garth L. Hallet suggests that from Plato until now, the intangibility of language has exercised a far more profound influence in philosophy than even Wittgenstein came close to demonstrating. Indeed, without that pervasive factor of language, the history of philosophy would have been undeniably different. Yet philosophy is, and can legitimately aspire to be, much more than a struggle between language and human comprehension of it. Ultimately, this book suggests that philosophy’s positive possibilities, so often obscured by linguistically-inattentive practice, reach as far as human thought can reach.

This eleventh edition was developed during the encyclopaedia's transition from a British to an American publication. Some of its articles were written by the best-known scholars of the time and it is considered to be a landmark encyclopaedia for scholarship and literary style.

This book offers a unique perspective on meaning in language, broadening the scope of existing understanding of meaning by introducing a comprehensive and cohesive account of meaning that draws on a wide range of linguistic approaches. The volume seeks to build up a complete picture of what meaning is, different types of meaning, and different ways of structuring the same meaning across myriad forms and varieties of language across such domains, such as everyday speech, advertising, humour, and academic writing. Supported by data from psycholinguistic and neurolinguistic research, the book combines different approaches from scholarship in semantics, including formalist, structuralist, cognitive, functionalist, and semiotics to demonstrate the ways in which meaning is expressed in words but also in word order and intonation. The book argues for a revised conceptualisation of meaning toward presenting a new perspective on semantics and its wider study in language and linguistic research. This book will appeal to scholars interested in meaning in language in such fields as linguistics, semantics, and semiotics. The Open Access version of this book, available at http://www.taylorfrancis.com has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives 4.0 license.

Cian J. Power explores how the biblical authors viewed and presented a fundamental human reality: the existence of the world's many languages. By examining explicit references to this diversity - such as the ambivalent account of its origins in the Tower of Babel episode - and implicit acknowledgements that included the use of strange-sounding speech to portray alien peoples, he illuminates ideas about Aramaic, Egyptian, Akkadian, and other ancient languages. Drawing on sociolinguistics, Power detects a consistent link between language and - ethnic, political, religious, and divine/human boundaries, and argues that changing historical circumstances are key to the Bible's varying attitudes. Furthermore, the study's findings regarding the biblical authors' ideas about their own language and its importance challenge our very notion of Hebrew.

"A delightful book … I should like to have written it myself." — Bertrand Russell First published in 1936, this first full-length presentation in English of the Logical Positivism of Carnap, Neurath, and others has gone through many printings to become a classic of thought and communication. It not only surveys one of the most important areas of modern thought; it also shows the confusion that arises from imperfect understanding of the uses of language. A first-rate antidote for fuzzy thought and muddled writing, this remarkable book has helped philosophers, writers, speakers, teachers, students, and general readers alike. Mr. Ayers sets up specific tests by which you can easily evaluate statements of ideas. You will also learn how to distinguish ideas that cannot be verified by experience — those expressing religious, moral, or aesthetic experience, those expounding theological or metaphysical doctrine, and those dealing with a priori truth. The basic thesis of this work is that philosophy should not squander its energies upon the unknowable, but should perform its proper function in criticism and analysis.

Definiteness has been a central topic in theoretical semantics since its modern foundation. However, despite its significance, there has been surprisingly scarce research on its cross-linguistic expression. With the purpose of contributing to filling this gap, the present volume gathers thirteen studies exploiting insights from formal semantics and syntax, typological and language specific studies, and, crucially, semantic fieldwork and cross-linguistic semantics, in order to address the expression and interpretation of definiteness in a diverse group of languages, most of them understudied. The papers presented in this volume aim to establish a dialogue between theory and data in order to answer the following questions: What formal strategies do natural languages employ to encode definiteness? What are the possible meanings associated to this notion across languages? Are there different types of definite reference? Which other functions (besides marking definite reference) are associated with definite descriptions? Each of the papers contained in this volume addresses at least one of these questions and, in doing so, they aim to enrich our understanding of definiteness.

Data-driven experimental analysis has become the main evaluation tool of Natural Language Processing (NLP) algorithms. In fact, in the last decade, it has become rare to see an NLP paper, particularly one that proposes a new algorithm, that does not include extensive experimental analysis, and the number of involved tasks, datasets, domains, and languages is constantly growing. This emphasis on empirical results highlights the role of statistical significance testing in NLP research: If we, as a community, rely on empirical evaluation to validate our hypotheses and reveal the correct language processing mechanisms, we better be sure that our results are not coincidental. The goal of this book is to discuss the main aspects of statistical significance testing in NLP. Our guiding assumption throughout the book is that the basic question NLP researchers and engineers deal with is whether or not one algorithm can be considered better than another one. This question drives the field forward as it allows the constant progress of developing better technology for language processing challenges. In practice, researchers and engineers would like to draw the right conclusion from a limited set of experiments, and this conclusion should hold for other experiments with datasets they do not have at their disposal or that they cannot perform due to limited time and resources. The book hence discusses the opportunities and challenges in using statistical significance testing in NLP, from the point of view of experimental comparison between two algorithms. We cover topics such as choosing an appropriate significance test for the major NLP tasks, dealing with the unique aspects of significance testing for non-convex deep neural networks, accounting for a large number of comparisons between two NLP algorithms in a statistically valid manner (multiple hypothesis testing), and, finally, the unique challenges yielded by the nature of the data and practices of the field.