quantitative corpus linguistics with r a practical introduction

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Quantitative Corpus Linguistics With R

Author : Stefan Th. Gries
ISBN : 9781135895594
Genre : Education
File Size : 82. 12 MB
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The first textbook of its kind, Quantitative Corpus Linguistics with R demonstrates how to use the open source programming language R for corpus linguistic analyses. Computational and corpus linguists doing corpus work will find that R provides an enormous range of functions that currently require several programs to achieve – searching and processing corpora, arranging and outputting the results of corpus searches, statistical evaluation, and graphing.

A Mosaic Of Corpus Linguistics

Author : Aquilino Sánchez
ISBN : 3631587899
Genre : Language Arts & Disciplines
File Size : 42. 68 MB
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The growth experienced by Corpus Linguistics over the last two decades has complicated the definition of the discipline. There is at present no consensus as to what corpus linguistics exactly is. Is it a methodology, a theoretical framework, a research paradigm? The goal of this book is multi-purpose. It provides material for a discussion of the notion of -corpus linguistics-, an overt discussion of the limits of this discipline and a comparison of some of the main approaches. And at the same time it offers a collection of selected papers representative of a range of approaches and applications associated with corpus research."

Text Analysis With R For Students Of Literature

Author : Matthew Jockers
ISBN : 9783319031644
Genre : Computers
File Size : 85. 73 MB
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Text Analysis with R for Students of Literature is written with students and scholars of literature in mind but will be applicable to other humanists and social scientists wishing to extend their methodological tool kit to include quantitative and computational approaches to the study of text. Computation provides access to information in text that we simply cannot gather using traditional qualitative methods of close reading and human synthesis. Text Analysis with R for Students of Literature provides a practical introduction to computational text analysis using the open source programming language R. R is extremely popular throughout the sciences and because of its accessibility, R is now used increasingly in other research areas. Readers begin working with text right away and each chapter works through a new technique or process such that readers gain a broad exposure to core R procedures and a basic understanding of the possibilities of computational text analysis at both the micro and macro scale. Each chapter builds on the previous as readers move from small scale “microanalysis” of single texts to large scale “macroanalysis” of text corpora, and each chapter concludes with a set of practice exercises that reinforce and expand upon the chapter lessons. The book’s focus is on making the technical palatable and making the technical useful and immediately gratifying.

Variationist Sociolinguistics

Author : Sali A. Tagliamonte
ISBN : 9781444344455
Genre : Language Arts & Disciplines
File Size : 63. 39 MB
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Variationist Sociolinguistics: Change, Observation, Interpretation presents a comprehensive, intermediate level examination of Language Variation and Change, the branch of sociolinguistics concerned with linguistic variation in spoken and written language. Represents the most up-to-date coverage of the history, developments, and methodologies of variationist sociolinguistics Addresses all aspects of linguistic variation, including areas not usually covered in introductory texts, e.g. the phonological, morpho-syntactic, discourse/pragmatic Outlines comparative sociolinguistic approach, data collection, methodological issues; and addresses state-of-the-art contemporary quantitative methods and statistical practice Features cutting-edge research at an appropriate level to facilitate student learning Engages students throughout with a variety of pedagogical features, including Mini Quizzes to test comprehension, extensive Exercises at the end of each chapter, the opportunity to do hands-on quantitative analysis of a never-before published data set, and Notes and Tips that offer insight into conducting sociolinguistic research. Extra materials and answers to the exercises are available at www.wiley.com/go/tagliamonte

The Oxford Handbook Of Corpus Phonology

Author : Jacques Durand
ISBN : 9780191669279
Genre : Language Arts & Disciplines
File Size : 49. 56 MB
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This handbook presents the first systematic account of corpus phonology - the employment of corpora for studying speakers' and listeners' acquisition and knowledge of the sound system of their native languages and the principles underlying those systems. The first part of the book discusses the design, compilation, and use of phonological corpora, while the second looks at specific applications. Part 3 presents the tools and methods used, while the final part examines a number of currently available phonological corpora in various languages. It will appeal not only to those working with phonological corpora, but also to researchers and students of phonology and phonetics more generally, as well as to all those interested in language variation, dialectology, language acquisition, and sociolinguistics.

Web And Network Data Science

Author : Thomas W. Miller
ISBN : 9780133887648
Genre : Computers
File Size : 68. 43 MB
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Master modern web and network data modeling: both theory and applications. In Web and Network Data Science, a top faculty member of Northwestern University’s prestigious analytics program presents the first fully-integrated treatment of both the business and academic elements of web and network modeling for predictive analytics. Some books in this field focus either entirely on business issues (e.g., Google Analytics and SEO); others are strictly academic (covering topics such as sociology, complexity theory, ecology, applied physics, and economics). This text gives today's managers and students what they really need: integrated coverage of concepts, principles, and theory in the context of real-world applications. Building on his pioneering Web Analytics course at Northwestern University, Thomas W. Miller covers usability testing, Web site performance, usage analysis, social media platforms, search engine optimization (SEO), and many other topics. He balances this practical coverage with accessible and up-to-date introductions to both social network analysis and network science, demonstrating how these disciplines can be used to solve real business problems.

Modeling Techniques In Predictive Analytics

Author : Thomas W. Miller
ISBN : 9780133886191
Genre : Computers
File Size : 72. 44 MB
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To succeed with predictive analytics, you must understand it on three levels: Strategy and management Methods and models Technology and code This up-to-the-minute reference thoroughly covers all three categories. Now fully updated, this uniquely accessible book will help you use predictive analytics to solve real business problems and drive real competitive advantage. If you’re new to the discipline, it will give you the strong foundation you need to get accurate, actionable results. If you’re already a modeler, programmer, or manager, it will teach you crucial skills you don’t yet have. Unlike competitive books, this guide illuminates the discipline through realistic vignettes and intuitive data visualizations–not complex math. Thomas W. Miller, leader of Northwestern University’s pioneering program in predictive analytics, guides you through defining problems, identifying data, crafting and optimizing models, writing effective R code, interpreting results, and more. Every chapter focuses on one of today’s key applications for predictive analytics, delivering skills and knowledge to put models to work–and maximize their value. Reflecting extensive student and instructor feedback, this edition adds five classroom-tested case studies, updates all code for new versions of R, explains code behavior more clearly and completely, and covers modern data science methods even more effectively. All data sets, extensive R code, and additional examples available for download at http://www.ftpress.com/miller If you want to make the most of predictive analytics, data science, and big data, this is the book for you. Thomas W. Miller’s unique balanced approach combines business context and quantitative tools, appealing to managers, analysts, programmers, and students alike. Miller addresses multiple business cases and challenges, including segmentation, brand positioning, product choice modeling, pricing research, finance, sports, text analytics, sentiment analysis, and social network analysis. He illuminates the use of cross-sectional data, time series, spatial, and spatio-temporal data. You’ll learn why each problem matters, what data are relevant, and how to explore the data you’ve identified. Miller guides you through conceptually modeling each data set with words and figures; and then modeling it again with realistic R programs that deliver actionable insights. You’ll walk through model construction, explanatory variable subset selection, and validation, mastering best practices for improving out-of-sample predictive performance. Throughout, Miller employs data visualization and statistical graphics to help you explore data, present models, and evaluate performance. This edition adds five new case studies, updates all code for the newest versions of R, adds more commenting to clarify how the code works, and offers a more detailed and up-to-date primer on data science methods. Gain powerful, actionable, profitable insights about: Advertising and promotion Consumer preference and choice Market baskets and related purchases Economic forecasting Operations management Unstructured text and language Customer sentiment Brand and price Sports team performance And much more

Modeling Techniques In Predictive Analytics With Python And R

Author : Thomas W. Miller
ISBN : 9780133892147
Genre : Computers
File Size : 69. 81 MB
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Master predictive analytics, from start to finish Start with strategy and management Master methods and build models Transform your models into highly-effective code—in both Python and R This one-of-a-kind book will help you use predictive analytics, Python, and R to solve real business problems and drive real competitive advantage. You’ll master predictive analytics through realistic case studies, intuitive data visualizations, and up-to-date code for both Python and R—not complex math. Step by step, you’ll walk through defining problems, identifying data, crafting and optimizing models, writing effective Python and R code, interpreting results, and more. Each chapter focuses on one of today’s key applications for predictive analytics, delivering skills and knowledge to put models to work—and maximize their value. Thomas W. Miller, leader of Northwestern University’s pioneering program in predictive analytics, addresses everything you need to succeed: strategy and management, methods and models, and technology and code. If you’re new to predictive analytics, you’ll gain a strong foundation for achieving accurate, actionable results. If you’re already working in the field, you’ll master powerful new skills. If you’re familiar with either Python or R, you’ll discover how these languages complement each other, enabling you to do even more. All data sets, extensive Python and R code, and additional examples available for download at http://www.ftpress.com/miller/ Python and R offer immense power in predictive analytics, data science, and big data. This book will help you leverage that power to solve real business problems, and drive real competitive advantage. Thomas W. Miller’s unique balanced approach combines business context and quantitative tools, illuminating each technique with carefully explained code for the latest versions of Python and R. If you’re new to predictive analytics, Miller gives you a strong foundation for achieving accurate, actionable results. If you’re already a modeler, programmer, or manager, you’ll learn crucial skills you don’t already have. Using Python and R, Miller addresses multiple business challenges, including segmentation, brand positioning, product choice modeling, pricing research, finance, sports, text analytics, sentiment analysis, and social network analysis. He illuminates the use of cross-sectional data, time series, spatial, and spatio-temporal data. You’ll learn why each problem matters, what data are relevant, and how to explore the data you’ve identified. Miller guides you through conceptually modeling each data set with words and figures; and then modeling it again with realistic code that delivers actionable insights. You’ll walk through model construction, explanatory variable subset selection, and validation, mastering best practices for improving out-of-sample predictive performance. Miller employs data visualization and statistical graphics to help you explore data, present models, and evaluate performance. Appendices include five complete case studies, and a detailed primer on modern data science methods. Use Python and R to gain powerful, actionable, profitable insights about: Advertising and promotion Consumer preference and choice Market baskets and related purchases Economic forecasting Operations management Unstructured text and language Customer sentiment Brand and price Sports team performance And much more

Revista Brasileira De Ling Stica Aplicada

Author :
ISBN : STANFORD:36105214162617
Genre : Applied linguistics
File Size : 89. 28 MB
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Frequency Effects In Language Learning And Processing

Author : Stefan Thomas Gries
ISBN : UCBK:C105612472
Genre : Language Arts & Disciplines
File Size : 68. 80 MB
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The volume contains a collection of studies on how the analysis of corpus and psycholinguistic data reveal how linguistic knowledge is affected by the frequency of linguistic elements/stimuli. The studies explore a wide range of phenomena, from phonological reduction processes and palatalization to morphological productivity, diachronic change, adjective preposition constructions, auxiliary omission, and multi-word units. The languages studied are Spanish and artificial languages, Russian, Dutch, and English. The sister volume focuses on language representation.

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