Input a Word, Analyze the World represents current perspectives on Corpus Linguistics (CL) from a variety of linguistic subdisciplines. Corpus Linguistics has proven itself an excellent methodology for the study of language variation and change, and is well-suited for interdisciplinary collaboration, as shown by the studies in this volume. Its title is inspired by the use of CL to assess language in different registers and with a variety of purposes. This collection contains thirty contributions by scholars in the field from across the globe, dealing with current topics on corpus production and corpus tools; lexical analysis, phraseology and grammar; translation and contrastive linguistics; and language learning. Language specialists will find these papers inspiring, as they present new insights on aspects related to research and teaching.

Analysing language data systematically and looking closely at how people formulate their thoughts can reveal astonishing insights about the human mind. Without presupposing specific subject knowledge, this book gently introduces its readers to theoretical insights as well as practical principles for systematic linguistic analysis from a cognitive perspective. Drawing on Thora Tenbrink's twenty years' experience in both linguistics and cognitive science, this book offers theoretical guidance and practical advice for doing cognitive discourse analysis. It covers areas of analysis as diverse as attention, perspective, granularity, certainty, inference, transformation, communication, and cognitive strategies, using inspiring examples from many different projects. Simple techniques and tools are used to allow readers new to the subject easy ways to apply the methods, without the need for complex technologies, whilst the cross-disciplinary approach can be applied to a diverse range of research purposes and contexts in which language and thought play a role.

This introductory textbook presents a variety of approaches and perspectives that can be employed to analyze any sample of discourse. The perspectives come from multiple disciplines, including linguistics, sociolinguistics, and linguistic anthropology, all of which shed light on meaning and the interactional construction of meaning through language use. Students without prior experience in discourse analysis will appreciate and understand the micro-macro relationship of language use in everyday contexts, in professional and academic settings, in languages other than English, and in a wide variety of media outlets. Each chapter is supported by examples of spoken and written discourse from various types of data sources, including conversations, commercials, university lectures, textbooks, print ads, and blogs, and concludes with hands-on opportunities for readers to actually do discourse analysis on their own. Students can also utilize the book’s comprehensive companion website, with flash cards for key terms, quizzes, and additional data samples, for in-class activities and self-study. With its accessible multi-disciplinary approach and comprehensive data samples from a variety of sources, Discourse Analysis is the ideal core text for the discourse analysis course in applied linguistics, English, education, and communication programs.

A textbook and practical guide for acquiring skills necessary to analyze the morphology and syntax of languages around the world.

Born in a time of anxiety, Words and Worlds examines some of the disquieting challenges that societies now face. Through an inquiry into a political lexicon of commonsense words, ranging from democracy and revolution to knowledge and authority, from inequality and toleration to war and power, the contributors to this book trouble the self-evidence of these terms, bringing into view the hidden transcripts and unexpected trajectories of many settled ideas, such as the human sense of belonging or the call for openness and transparency in research and public life. The case studies conducted over five continents with the tools of eight different disciplines challenge the ethnocentric assumptions, false moralism, and cultural prejudices that underlie much discussion on corruption or even the virtue invested in resilience. The critique of the ubiquitous use of crisis to characterize our times shows how this framing obscures the unjust conditions of existence and the violence of everyday life. Together the essays in this volume offer a fresh look at the deeply connected worlds we inhabit in solidarity and in discord. Contributors. Banu Bargu, Veena Das, Alex de Waal, Didier Fassin, Peter Geschiere, Behrooz Ghamari-Tabrizi, Caroline Humphrey, Ravi Kanbur, Julieta Lemaitre, Uday S. Mehta, Jan-Werner Müller, Jonathan Pugh, Elizabeth F. Sanders, Todd Sanders

Human-Computer Interaction: An Empirical Research Perspective is the definitive guide to empirical research in HCI. The book begins with foundational topics including an historical context, the human factor, interaction elements, and the fundamentals of science and research. From there, readers will progress to learning about the methods for conducting an experiment to evaluate a new computer interface or interaction technique. There are detailed discussions and how-to analyses on models of interaction, focusing on descriptive models and predictive models. Writing and publishing a research paper is explored with helpful tips for success. Throughout the book, readers will find hands-on exercises, checklists, and real-world examples. This is a must-have, comprehensive guide to empirical and experimental research in HCI – an essential addition to your HCI library. - Provides a master, A-to-Z guide in a concise, hands-on reference - Presents the practical and theoretical ins-and-outs of user studies - Includes exercises, takeaway points, and case studies throughout - Updated to incorporate developments in HCI, including Human performance outliers, Interaction elements: pointing and selecting; text input; gesture input

The objective of Document Analysis and Recognition (DAR) is to recognize the text and graphical components of a document and to extract information. This book is a collection of research papers and state-of-the-art reviews by leading researchers all over the world. It includes pointers to challenges and opportunities for future research directions. The main goal of the book is to identify good practices for the use of learning strategies in DAR.

For the last four decades, the uses of econometric tools and techniques, irrespective of discipline, have been increasing rapidly for solving problems, appropriate decision-making and policy formulation. Generally, it is accepted that the study of modern business and economics is incomplete without a proper knowledge of econometric tools and techniques. In view of the increasing complexity and variety of problems in business and economics, students and researchers may not be able to cope and hence may remain unfamiliar with many aspects of business and economic problems. Thus, this book was written to explore basic and advanced studies of econometrics for undergraduate and graduate students of business and economics, as well as for researchers already engaged in these fields who require an introduction to econometric methods and their application for solving of real-life problems. The book offers a balanced presentation of fundamental and advanced levels of econometric concepts and methods, along with practical examples of their effective application in real-life problems.

This book constitutes the proceedings of the 22nd International Semantic Web Conference, ISWC 2023, which took place in October 2023 in Athens, Greece. The 58 full papers presented in this double volume were thoroughly reviewed and selected from 248 submissions. Many submissions focused on the use of reasoning and query answering, witha number addressing engineering, maintenance, and alignment tasks for ontologies. Likewise, there has been a healthy batch of submissions on search, query, integration, and the analysis of knowledge. Finally, following the growing interest in neuro-symbolic approaches, there has been a rise in the number of studies that focus on the use of Large Language Models and Deep Learning techniques such as Graph Neural Networks.