Quantitative Corpus Linguistics with R: A Practical Introduction [NOOK Book]

Overview

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.

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Quantitative Corpus Linguistics with R: A Practical Introduction

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Overview

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.

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Editorial Reviews

From the Publisher
"This book is a must for every corpus linguist's desk right beside the computer - that's where it does its wonders." - International Journal of Corpus Linguistics
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Product Details

  • ISBN-13: 9781135895594
  • Publisher: Taylor & Francis
  • Publication date: 3/4/2009
  • Sold by: Barnes & Noble
  • Format: eBook
  • Pages: 256
  • File size: 2 MB

Meet the Author

Stefan Th. Gries is Professor of Linguistics at the University of California, Santa Barbara, USA.

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Table of Contents

Acknowledgments viii

1 Introduction 1

1.1 Why Another Introduction to Corpus Linguistics? 1

1.2 Outline of the Book 4

1.3 Recommendation for Instructors 5

2 The Three Central Corpus-linguistic Methods 7

2.1 Corpora 7

2.1.1 What is a Corpus? 7

2.1.2 What Kinds of Corpora are There? 9

2.2 Frequency Lists 12

2.3 Lexical Co-occurrence: Collocations 14

2.4 (Lexico-) Grammatical Co-occurrence: Concordances 16

3 An Introduction to R 19

3.1 A Few Central Notions: Data Structures, Functions, and Arguments 23

3.2 Vectors 28

3.2.1 Basics 28

3.2.2 Loading Vectors 32

3.2.3 Accessing and Processing (Parts of) Vectors 35

3.2.4 Saving Vectors 42

3.3 Factors 43

3.4 Data Frames 44

3.4.1 Generating Data Frames 44

3.4.2 Loading and Saving Data Frames 46

3.4.3 Accessing and Processing (Parts of) Data Frames 48

3.5 Lists 53

3.6 Elementary Programming Functions 59

3.6.1 Conditional Expressions 59

3.6.2 Loops 60

3.6.3 Rules of Programming 64

3.7 Character/String Processing 68

3.7.1 Getting Information from and Accessing (Vectors of) Character Strings 69

3.7.2 Elementary Ways to Change (Vectors of) Character Strings 70

3.7.3 Merging and Splitting (Vectors of) Character Strings without Regular Expressions 70

3.7.4 Searching and Replacing without Regular Expressions 72

3.7.5 Searching and Replacing with Regular Expressions 79

3.7.6 Merging and Splitting (Vectors of) Character Strings with Regular Expressions 96

3.8 File and Directory Operations 99

4 Using R in Corpus Linguistics 105

4.1 Frequency Lists 106

4.1.1 A Frequency List of an Unannotated Corpus 106

4.1.2 A Reverse Frequency List of an Unannotated Corpus 110

4.1.3A Frequency List of an Annotated Corpus 112

4.1.4 A Frequency List of Tag-word Sequences from an Annotated Corpus 114

4.1.5 A Frequency List of Word Pairs from an Annotated Corpus 118

4.1.6 A Frequency List of an Annotated Corpus (with One Word Per Line) 124

4.1.7 A Frequency List of Word Pairs of an Annotated Corpus (with One Word Per Line) 126

4.2 Concordances 127

4.2.1 A Concordance of an Unannotated Text File 127

4.2.2 A Simple Concordance from Files of a POS-tagged (SGML) Corpus 135

4.2.3 More Complex Concordances from Files of a POS-tagged (SGML) Corpus 141

4.2.4 A Lemma-based Concordance from Files of a POS-tagged and Lemmatized (XML) Corpus 146

4.3 Collocations 149

4.4 Excursus 1: Processing Multi-tiered Corpora 156

4.5 Excursus 2: Unicode 166

4.5.1 Frequency Lists 167

4.5.2 Concordancing 169

5 Some Statistics for Corpus Linguistics 173

5.1 Introduction to Statistical Thinking 174

5.1.1 Variables and their Roles in an Analysis 174

5.1.2 Variables and their Information Value 174

5.1.3 Hypotheses: Formulation and Operationalization 176

5.1.4 Data Analysis 182

5.1.5 Hypothesis (and Significance) Testing 183

5.2 Categorical Dependent Variables 189

5.2.1 One Categorical Dependent Variable, No Independent Variable 189

5.2.2 One Categorical Dependent Variable, One Categorical Independent Variable 192

5.2.3 One Categorical Dependent Variable, 2+ Independent Variables 200

5.3 Interval/Ratio-scaled Dependent Variables 201

5.3.1 Descriptive Statistics for Interval/Ratio-scaled Dependent Variables 201

5.3.2 One Interval/Ratio-scaled Dependent Variable, One Categorical Independent Variable 205

5.3.3 One Interval/Ratio-scaled Dependent Variable, One Interval/Ratio-scaled Independent Variable 211

5.3.4 One Interval/Ratio-scaled Dependent Variable, 2+ Independent Variables 214

5.4 Customizing Statistical Plots 215

5.5 Reporting Results 215

6 Case Studies and Pointers to Other Applications 219

6.1 Introduction to the Case Studies 219

6.2 Some Pointers to Further Applications 220

Appendix 225

References 229

Endnotes 237

Index 243

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