A comprehensive and accessible introduction to statistics in corpus linguistics, covering multiple techniques of quantitative language analysis and data visualisation.

Traditional approaches focused on significance tests have often been difficult for linguistics researchers to visualise. Statistics in Corpus Linguistics Research: A New Approach breaks these significance tests down for researchers in corpus linguistics and linguistic analysis, promoting a visual approach to understanding the performance of tests with real data, and demonstrating how to derive new intervals and tests. Accessibly written, this book discusses the ‘why’ behind the statistical model, allowing readers a greater facility for choosing their own methodologies. Accessibly written for those with little to no mathematical or statistical background, it explains the mathematical fundamentals of simple significance tests by relating them to confidence intervals. With sample datasets and easy-to-read visuals, this book focuses on practical issues, such as how to: • pose research questions in terms of choice and constraint; • employ confidence intervals correctly (including in graph plots); • select optimal significance tests (and what results mean); • measure the size of the effect of one variable on another; • estimate the similarity of distribution patterns; and • evaluate whether the results of two experiments significantly differ. Appropriate for anyone from the student just beginning their career to the seasoned researcher, this book is both a practical overview and valuable resource.

This book in the Edinburgh Textbooks in Empirical Linguistics series is a comprehensive introduction to the statistics currently used in corpus linguistics. Statistical techniques and corpus applications - whether oriented towards linguistics or language engineering - often go hand in glove, and corpus linguists have used an increasingly wide variety of statistics, drawing on techniques developed in a great many fields. This is the first one-volume introduction to the subject.

By contrasting different approaches and datasets, this book highlights critical developments in latest corpus-linguistic research.

Corpus linguistics is the study of language data on a large scale - the computer-aided analysis of very extensive collections of transcribed utterances or written texts. This textbook outlines the basic methods of corpus linguistics, explains how the discipline of corpus linguistics developed and surveys the major approaches to the use of corpus data. It uses a broad range of examples to show how corpus data has led to methodological and theoretical innovation in linguistics in general. Clear and detailed explanations lay out the key issues of method and theory in contemporary corpus linguistics. A structured and coherent narrative links the historical development of the field to current topics in 'mainstream' linguistics. Practical tasks and questions for discussion at the end of each chapter encourage students to test their understanding of what they have read and an extensive glossary provides easy access to definitions of technical terms used in the text.

This textbook examines empirical linguistics from a theoretical linguist’s perspective. It provides both a theoretical discussion of what quantitative corpus linguistics entails and detailed, hands-on, step-by-step instructions to implement the techniques in the field. The statistical methodology and R-based coding from this book teach readers the basic and then more advanced skills to work with large data sets in their linguistics research and studies. Massive data sets are now more than ever the basis for work that ranges from usage-based linguistics to the far reaches of applied linguistics. This book presents much of the methodology in a corpus-based approach. However, the corpus-based methods in this book are also essential components of recent developments in sociolinguistics, historical linguistics, computational linguistics, and psycholinguistics. Material from the book will also be appealing to researchers in digital humanities and the many non-linguistic fields that use textual data analysis and text-based sensorimetrics. Chapters cover topics including corpus processing, frequencing data, and clustering methods. Case studies illustrate each chapter with accompanying data sets, R code, and exercises for use by readers. This book may be used in advanced undergraduate courses, graduate courses, and self-study.

As in its first edition, the new edition of Quantitative Corpus Linguistics with R demonstrates how to process corpus-linguistic data with the open-source programming language and environment R. Geared in general towards linguists working with observational data, and particularly corpus linguists, it introduces R programming with emphasis on: data processing and manipulation in general; text processing with and without regular expressions of large bodies of textual and/or literary data, and; basic aspects of statistical analysis and visualization. This book is extremely hands-on and leads the reader through dozens of small applications as well as larger case studies. Along with an array of exercise boxes and separate answer keys, the text features a didactic sequential approach in case studies by way of subsections that zoom in to every programming problem. The companion website to the book contains all relevant R code (amounting to approximately 7,000 lines of heavily commented code), most of the data sets as well as pointers to others, and a dedicated Google newsgroup. This new edition is ideal for both researchers in corpus linguistics and instructors who want to promote hands-on approaches to data in corpus linguistics courses.

The Cambridge Handbook of English Corpus Linguistics (CHECL) surveys the breadth of corpus-based linguistic research on English, including chapters on collocations, phraseology, grammatical variation, historical change, and the description of registers and dialects. The most innovative aspects of the CHECL are its emphasis on critical discussion, its explicit evaluation of the state of the art in each sub-discipline, and the inclusion of empirical case studies. While each chapter includes a broad survey of previous research, the primary focus is on a detailed description of the most important corpus-based studies in this area, with discussion of what those studies found, and why they are important. Each chapter also includes a critical discussion of the corpus-based methods employed for research in this area, as well as an explicit summary of new findings and discoveries.

Covering the major approaches to the use of corpus data, this work gathers together influential readings from leading names in the discipline, including Biber, Widdowson, Sinclair, Carter and McCarthy.