Python实战：自然语言处理入门指南

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"《使用Python进行自然语言处理》是一本由Steven Bird、Ewan Klein和Edward Loper合著的专业书籍，专为那些希望探索和实践自然语言处理(Natural Language Processing, NLP)技术的读者而编撰。这本书是针对Python编程语言进行NLP的详细介绍，适合对编程有兴趣且想在这个领域深入学习的开发者，特别是初学者。书中涵盖了Python在NLP领域的基础和进阶内容，包括但不限于词法分析、句法分析、语义理解和文本挖掘等核心概念。通过Python工具包如NLTK (Natural Language Toolkit) 和 SpaCy，作者展示了如何处理和理解人类语言，实现文本分类、情感分析、命名实体识别、机器翻译等实际应用场景。该书不仅提供了理论知识，还配以丰富的实例和实战项目，帮助读者逐步掌握Python NLP的实用技巧。版权方面，本书享有2009年的版权保护，并强调了O'Reilly Media出版的作品可以供教育、商业或销售推广使用，同时提供在线版本供读者选择。编辑Julie Steele、生产编辑Loranah Dimant、校对人员Genevieve'd'Entremont、证明阅读者Loranah Dimant以及指数员Ellen Troutman Zaig等专业人士参与了本书的制作过程，确保了内容的质量和准确性。封面设计由Karen Montgomery负责，内部设计则由David Futato操刀，插图由Robert Romano绘制，体现了O'Reilly Media一贯的高品质标准。《使用Python进行自然语言处理》于2009年6月首次出版，作为一本Nutshell Handbook（袖珍手册），它提供了一个简洁明快的学习路径，对于想要进入NLP领域或提升Python编程技能的读者来说，无疑是一本不可或缺的参考资料。"

NLTK-Data

This contains the linguistic corpora that are analyzed and processed in the book.

NumPy (recommended)

This is a scientific computing library with support for multidimensional arrays and

linear algebra, required for certain probability, tagging, clustering, and classifica-

tion tasks.

Matplotlib (recommended)

This is a 2D plotting library for data visualization, and is used in some of the book’s

code samples that produce line graphs and bar charts.

NetworkX (optional)

This is a library for storing and manipulating network structures consisting of

nodes and edges. For visualizing semantic networks, also install the Graphviz

library.

Prover9 (optional)

This is an automated theorem prover for first-order and equational logic, used to

support inference in language processing.

Natural Language Toolkit (NLTK)

NLTK was originally created in 2001 as part of a computational linguistics course in

the Department of Computer and Information Science at the University of Pennsylva-

nia. Since then it has been developed and expanded with the help of dozens of con-

tributors. It has now been adopted in courses in dozens of universities, and serves as

the basis of many research projects. Table P-2 lists the most important NLTK modules.

Table P-2. Language processing tasks and corresponding NLTK modules with examples of

functionality

Language processing task NLTK modules Functionality

Accessing corpora nltk.corpus Standardized interfaces to corpora and lexicons

String processing nltk.tokenize, nltk.stem Tokenizers, sentence tokenizers, stemmers

Collocation discovery nltk.collocations t-test, chi-squared, point-wise mutual information

Part-of-speech tagging nltk.tag n-gram, backoff, Brill, HMM, TnT

Classification nltk.classify, nltk.cluster Decision tree, maximum entropy, naive Bayes, EM, k-means

Chunking nltk.chunk Regular expression, n-gram, named entity

Parsing nltk.parse Chart, feature-based, unification, probabilistic, dependency

Semantic interpretation nltk.sem, nltk.inference Lambda calculus, first-order logic, model checking

Evaluation metrics nltk.metrics Precision, recall, agreement coefficients

Probability and estimation nltk.probability Frequency distributions, smoothed probability distributions

Applications nltk.app, nltk.chat Graphical concordancer, parsers, WordNet browser, chatbots

xiv | Preface

Language processing task NLTK modules Functionality

Linguistic fieldwork nltk.toolbox Manipulate data in SIL Toolbox format

NLTK was designed with four primary goals in mind:

Simplicity

To provide an intuitive framework along with substantial building blocks, giving

users a practical knowledge of NLP without getting bogged down in the tedious

house-keeping usually associated with processing annotated language data

Consistency

To provide a uniform framework with consistent interfaces and data structures,

and easily guessable method names

Extensibility

To provide a structure into which new software modules can be easily accommo-

dated, including alternative implementations and competing approaches to the

same task

Modularity

To provide components that can be used independently without needing to un-

derstand the rest of the toolkit

Contrasting with these goals are three non-requirements—potentially useful qualities

that we have deliberately avoided. First, while the toolkit provides a wide range of

functions, it is not encyclopedic; it is a toolkit, not a system, and it will continue to

evolve with the field of NLP. Second, while the toolkit is efficient enough to support

meaningful tasks, it is not highly optimized for runtime performance; such optimiza-

tions often involve more complex algorithms, or implementations in lower-level pro-

gramming languages such as C or C++. This would make the software less readable

and more difficult to install. Third, we have tried to avoid clever programming tricks,

since we believe that clear implementations are preferable to ingenious yet indecipher-

able ones.

For Instructors

Natural Language Processing is often taught within the confines of a single-semester

course at the advanced undergraduate level or postgraduate level. Many instructors

have found that it is difficult to cover both the theoretical and practical sides of the

subject in such a short span of time. Some courses focus on theory to the exclusion of

practical exercises, and deprive students of the challenge and excitement of writing

programs to automatically process language. Other courses are simply designed to

teach programming for linguists, and do not manage to cover any significant NLP con-

tent. NLTK was originally developed to address this problem, making it feasible to

cover a substantial amount of theory and practice within a single-semester course, even

if students have no prior programming experience.

Preface | xv

A significant fraction of any NLP syllabus deals with algorithms and data structures.

On their own these can be rather dry, but NLTK brings them to life with the help of

interactive graphical user interfaces that make it possible to view algorithms step-by-

step. Most NLTK components include a demonstration that performs an interesting

task without requiring any special input from the user. An effective way to deliver the

materials is through interactive presentation of the examples in this book, entering

them in a Python session, observing what they do, and modifying them to explore some

empirical or theoretical issue.

This book contains hundreds of exercises that can be used as the basis for student

assignments. The simplest exercises involve modifying a supplied program fragment in

a specified way in order to answer a concrete question. At the other end of the spectrum,

NLTK provides a flexible framework for graduate-level research projects, with standard

implementations of all the basic data structures and algorithms, interfaces to dozens

of widely used datasets (corpora), and a flexible and extensible architecture. Additional

support for teaching using NLTK is available on the NLTK website.

We believe this book is unique in providing a comprehensive framework for students

to learn about NLP in the context of learning to program. What sets these materials

apart is the tight coupling of the chapters and exercises with NLTK, giving students—

even those with no prior programming experience—a practical introduction to NLP.

After completing these materials, students will be ready to attempt one of the more

advanced textbooks, such as Speech and Language Processing, by Jurafsky and Martin

(Prentice Hall, 2008).

This book presents programming concepts in an unusual order, beginning with a non-

trivial data type—lists of strings—then introducing non-trivial control structures such

as comprehensions and conditionals. These idioms permit us to do useful language

processing from the start. Once this motivation is in place, we return to a systematic

presentation of fundamental concepts such as strings, loops, files, and so forth. In this

way, we cover the same ground as more conventional approaches, without expecting

readers to be interested in the programming language for its own sake.

Two possible course plans are illustrated in Table P-3. The first one presumes an arts/

humanities audience, whereas the second one presumes a science/engineering audi-

ence. Other course plans could cover the first five chapters, then devote the remaining

time to a single area, such as text classification (Chapters 6 and 7), syntax (Chapters

8 and 9), semantics (Chapter 10), or linguistic data management (Chapter 11).

Table P-3. Suggested course plans; approximate number of lectures per chapter

Chapter Arts and Humanities Science and Engineering

Chapter 1, Language Processing and Python 2–4 2

Chapter 2, Accessing Text Corpora and Lexical Resources 2–4 2

Chapter 3, Processing Raw Text 2–4 2

Chapter 4, Writing Structured Programs 2–4 1–2

xvi | Preface

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