Analizing Text with the Natural Language Toolkit
by Steven Bird, Ewan Klein, and Edward Loper
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Book Details
Price
|
2.50 |
|---|---|
Pages
| 504 p |
File Size
|
3,498 KB |
File Type
|
PDF format |
ISBN
| 978-0-596-51649-9 |
Copyright©
| 2009 Steven Bird, Ewan Klein, and Edward Loper. |
This is a book about Natural Language Processing. By “natural language” we mean a
language that is used for everyday communication by humans; languages such as English,
Hindi, or Portuguese. In contrast to artificial languages such as programming languages
and mathematical notations, natural languages have evolved as they pass from
generation to generation, and are hard to pin down with explicit rules. We will take
Natural Language Processing—or NLP for short—in a wide sense to cover any kind of
computer manipulation of natural language. At one extreme, it could be as simple as
counting word frequencies to compare different writing styles. At the other extreme,
NLP involves “understanding” complete human utterances, at least to the extent of
being able to give useful responses to them.
Technologies based on NLP are becoming increasingly widespread. For example,
phones and handheld computers support predictive text and handwriting recognition;
web search engines give access to information locked up in unstructured text; machine
translation allows us to retrieve texts written in Chinese and read them in Spanish. By
providing more natural human-machine interfaces, and more sophisticated access to
stored information, language processing has come to play a central role in the multilingual
information society.
This book provides a highly accessible introduction to the field of NLP. It can be used
for individual study or as the textbook for a course on natural language processing or
computational linguistics, or as a supplement to courses in artificial intelligence, text
mining, or corpus linguistics. The book is intensely practical, containing hundreds of
fully worked examples and graded exercises.
The book is based on the Python programming language together with an open source
library called the Natural Language Toolkit (NLTK). NLTK includes extensive software,
data, and documentation, all freely downloadable from http://www.nltk.org/.
Distributions are provided for Windows, Macintosh, and Unix platforms. We strongly
encourage you to download Python and NLTK,
and try out the examples and exercises along the way.
Audience
NLP is important for scientific, economic, social, and cultural reasons. NLP is experiencing
rapid growth as its theories and methods are deployed in a variety of new language
technologies. For this reason it is important for a wide range of people to have a
working knowledge of NLP. Within industry, this includes people in human-computer
interaction, business information analysis, and web software development. Within
academia, it includes people in areas from humanities computing and corpus linguistics
through to computer science and artificial intelligence. (To many people in academia,
NLP is known by the name of “Computational Linguistics.”)
This book is intended for a diverse range of people who want to learn how to write
programs that analyze written language, regardless of previous programming
experience:
New to programming?
The early chapters of the book are suitable for readers with no prior knowledge of
programming, so long as you aren’t afraid to tackle new concepts and develop new
computing skills. The book is full of examples that you can copy and try for yourself,
together with hundreds of graded exercises. If you need a more general introduction
to Python, see the list of Python resources at http://docs.python.org/.
New to Python?
Experienced programmers can quickly learn enough Python using this book to get
immersed in natural language processing. All relevant Python features are carefully
explained and exemplified, and you will quickly come to appreciate Python’s suitability
for this application area. The language index will help you locate relevant
discussions in the book.
Already dreaming in Python?
Skim the Python examples and dig into the interesting language analysis material
that starts in Chapter 1. You’ll soon be applying your skills to this fascinating domain.
Table of Contents
Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix
1. Language Processing and Python . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 Computing with Language: Texts and Words 1
1.2 A Closer Look at Python: Texts as Lists of Words 10
1.3 Computing with Language: Simple Statistics 16
1.4 Back to Python: Making Decisions and Taking Control 22
1.5 Automatic Natural Language Understanding 27
1.6 Summary 33
1.7 Further Reading 34
1.8 Exercises 35
2. Accessing Text Corpora and Lexical Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
2.1 Accessing Text Corpora 39
2.2 Conditional Frequency Distributions 52
2.3 More Python: Reusing Code 56
2.4 Lexical Resources 59
2.5 WordNet 67
2.6 Summary 73
2.7 Further Reading 73
2.8 Exercises 74
3. Processing Raw Text . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
3.1 Accessing Text from the Web and from Disk 80
3.2 Strings: Text Processing at the Lowest Level 87
3.3 Text Processing with Unicode 93
3.4 Regular Expressions for Detecting Word Patterns 97
3.5 Useful Applications of Regular Expressions 102
3.6 Normalizing Text 107
3.7 Regular Expressions for Tokenizing Text 109
3.8 Segmentation 112
3.9 Formatting: From Lists to Strings 116
3.10 Summary 121
3.11 Further Reading 122
3.12 Exercises 123
4. Writing Structured Programs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129
4.1 Back to the Basics 130
4.2 Sequences 133
4.3 Questions of Style 138
4.4 Functions: The Foundation of Structured Programming 142
4.5 Doing More with Functions 149
4.6 Program Development 154
4.7 Algorithm Design 160
4.8 A Sample of Python Libraries 167
4.9 Summary 172
4.10 Further Reading 173
4.11 Exercises 173
5. Categorizing and Tagging Words . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179
5.1 Using a Tagger 179
5.2 Tagged Corpora 181
5.3 Mapping Words to Properties Using Python Dictionaries 189
5.4 Automatic Tagging 198
5.5 N-Gram Tagging 202
5.6 Transformation-Based Tagging 208
5.7 How to Determine the Category of a Word 210
5.8 Summary 213
5.9 Further Reading 214
5.10 Exercises 215
6. Learning to Classify Text . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221
6.1 Supervised Classification 221
6.2 Further Examples of Supervised Classification 233
6.3 Evaluation 237
6.4 Decision Trees 242
6.5 Naive Bayes Classifiers 245
6.6 Maximum Entropy Classifiers 250
6.7 Modeling Linguistic Patterns 254
6.8 Summary 256
6.9 Further Reading 256
6.10 Exercises 257
7. Extracting Information from Text . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261
7.1 Information Extraction 261
7.2 Chunking 264
7.3 Developing and Evaluating Chunkers 270
7.4 Recursion in Linguistic Structure 277
7.5 Named Entity Recognition 281
7.6 Relation Extraction 284
7.7 Summary 285
7.8 Further Reading 286
7.9 Exercises 286
8. Analyzing Sentence Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 291
8.1 Some Grammatical Dilemmas 292
8.2 What’s the Use of Syntax? 295
8.3 Context-Free Grammar 298
8.4 Parsing with Context-Free Grammar 302
8.5 Dependencies and Dependency Grammar 310
8.6 Grammar Development 315
8.7 Summary 321
8.8 Further Reading 322
8.9 Exercises 322
9. Building Feature-Based Grammars . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 327
9.1 Grammatical Features 327
9.2 Processing Feature Structures 337
9.3 Extending a Feature-Based Grammar 344
9.4 Summary 356
9.5 Further Reading 357
9.6 Exercises 358
10. Analyzing the Meaning of Sentences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 361
10.1 Natural Language Understanding 361
10.2 Propositional Logic 368
10.3 First-Order Logic 372
10.4 The Semantics of English Sentences 385
10.5 Discourse Semantics 397
10.6 Summary 402
10.7 Further Reading 403
10.8 Exercises 404
11. Managing Linguistic Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 407
11.1 Corpus Structure: A Case Study 407
11.2 The Life Cycle of a Corpus 412
11.3 Acquiring Data 416
11.4 Working with XML 425
11.5 Working with Toolbox Data 431
11.6 Describing Language Resources Using OLAC Metadata 435
11.7 Summary 437
11.8 Further Reading 437
11.9 Exercises 438
Afterword: The Language Challenge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 441
Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 449
NLTK Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 459
General Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 463
Bookscreen
What You Will Learn
By digging into the material presented here, you will learn:
• How simple programs can help you manipulate and analyze language data, and
how to write these programs
• How key concepts from NLP and linguistics are used to describe and analyze language
• How data structures and algorithms are used in NLP
• How language data is stored in standard formats, and how data can be used to
evaluate the performance of NLP techniques
Depending on your background, and your motivation for being interested in NLP, you
will gain different kinds of skills and knowledge from this book, as set out in Table P-1.
Table P-1. Skills and knowledge to be gained from reading this book,
depending on readers’ goals and background