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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.
- Sales Rank: #1441764 in Books
- Published on: 2009-02-22
- Released on: 2009-03-26
- Original language: English
- Number of items: 1
- Dimensions: 11.00" h x .59" w x 8.50" l, 1.00 pounds
- Binding: Paperback
- 260 pages
Review
"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
About the Author
Stefan Th. Gries is Professor of Linguistics at the University of California, Santa Barbara, USA.
Most helpful customer reviews
15 of 15 people found the following review helpful.
Do not use as a primary text
By a reader
A non-programmer friend took a course with this book and asked me for help in learning R. I was surprised, since my experience was that corpus linguistics people rarely do any programming. Besides, I never thought of R as a tool for text processing.
Chapter One, 6 pages. In the introduction, the author claims that R is easier than Perl or Python for manipulating text. He argues that being able to program will allow you to do more interesting stuff than you can do with just a concordancing program.
Chapter Two, 12 pages. This is a concise explanation of what is involved in corpus-based analysis. He argues that all of corpus analysis boils down to frequency lists, collocation lists, and concordances. The two main non-simple issues are defining what a word is and processing tagged text.
Chapter Three, 86 pages. His whirlwind survey of essential features of R and regular expressions does not have a clear structure. There is far too little development going from simple to complex examples. Even knowing some R, I found the explanations required much rereading. I heard the students in the above mentioned course were extremely frustrated.
I agree with another reviewer that the code listings are badly displayed. More importantly, conventions used are not consistent through the chapters and accompanying web site, and are not consistent with good practice in the R community. There are many deeply nested functions, overuse of periods in names, a lack of whitespace, and long comments as single lines.
Chapter Four, 68 pages. Here is the justification for the book, examples of using R in corpus linguistics. They are all simple tasks using regular expressions to filter combinations of words and tags and to format output lines. What I learned from this chapter is that R is an inappropriate tool for text manipulation. First, R has few string-handling functions, so string operations take more steps than in the popular scripting languages. Second, manipulation of text in vectors requires a lot more bookeeping than in a string oriented language. Overall, R requires much more work and non-obvious intermediate steps. As for the writing, I found the explanations in this chapter particularly long-winded and roundabout.
Chapter Five, 46 pages. This is an overview of the basic ideas of statistical analysis, mostly independent of the rest of the book. This kind of chapter is common in textbooks and I have a strong objection to them. In such a brief introduction, many things are introduced without explanation, and ideas come too fast for the total newcomer to appreciate them. This chapter is a typical example.
Chapter Six, 6 pages. He gives a brief introduction to the data sets and exercises on the book's web site. He ends by mentioning several research topics in computational linguistics that utilize more sophisticated processing than is normally used in corpus linguistics.
In summary, chapters two and six were interesting, but the rest should not be used for primary learning material. One thing I did appreciate was the scattered examples of gotchas in processing corpora.
If you are using this text, this supplementary reading will help. For regular expressions, the most clear and simple book is Sams Teach Yourself Regular Expressions in 10 Minutes. If you have much computing experience, you may like the added context given in Introducing Regular Expressions. For an R tutorial, R For Dummies covers all you will need, but those with programming experience might prefer The Art of R Programming. Since text handling is a minor concern for the typical R user, it gets little treatment in books, so expect to read the R documentation for details on the relevant functions.
11 of 12 people found the following review helpful.
Considerate Corpus Linguistics and R Introduction
By John M. Ford
This book was not just written, it was designed--and well designed to teach readers about both corpus linguistics and the R statistics package. This electronic version allows busy readers with Kindles or iPhones to study productively during long commutes, even longer delays at the doctor's office, and endless unpunctuated speeches by boorish colleagues in staff meeting.
The six chapters of Stephan Th. Gries' book unfold in an instructionally sound sequence. Chapter 1 briefly introduces corpus linguistics and directs readers elsewhere for a thorough treatment of its history and theories. The second chapter defines word frequency lists, word collocations, and concordances--three basic analysis tools of the discipline. Chapter 3 shifts focus and introduces the R software. This is an excellent and thorough coverage of R data manipulation, programming, and the text processing needed to analyze linguistic corpora.
Building on this foundation, the book integrates R with linguistic analysis. Chapter 4 revisits word lists, collocations and concordances, teaching readers to implement these methods in R. Chapter 5 reviews the basics of statistical reasoning and introduces additional analysis techniques in R. Chapter 6 presents case studies and points readers to the book's supporting web site for associated data files and R syntax files.
The book is considerate of the reader. It uses only freely-available, open source software such as R, the Tinn-R text editor, and OpenOffice Calc. Beyond the price of the book and access to a computer, the author intends no financial barriers to learning. The micro-design of the chapters is also reader-friendly. R code and output are clearly marked and helpfully annotated. Frequent "Think Breaks" in the chapters challenge readers to solve a small problem before reading the answer. This technique encourages active reading and produces a feeling of satisfaction as one progresses through each section. Chapters end with "For further study/exploration" sections that contain pointers to R documentation, web resources, and further reading.
I recommend this book as a self-contained source about statistical methods in corpus linguistics as implemented in R. I would supplement it with Svenja Adolphs' Introducing Electronic Text Analysis to learn how the results of statistical analysis are used in applied linguistics and related disciplines.
0 of 0 people found the following review helpful.
Code presentation is very poor. Structure of each section is very difficult to ...
By Peter Ellis
I came to this book as an R expert wanting to apply it to linguistics which is new to me, so I'm not quite the target audience.
R explanation is difficult to follow. R approach seems idiosyncratic and depends heavily on a few techniques. Code presentation is very poor. Structure of each section is very difficult to follow. Statistics and R are very basic. My recommendation is to learn linguistics elsewhere, learn R elsewhere, and combine them yourself.
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