《UNIX网络编程(卷1):套接字联网API(第3版)》中顶级网络编程专家Bill Fenner和Andrew M. Rudoff应邀执笔,对W. Richard Stevens的经典作品进行修订。书中吸纳了近几年网络技术的发展,增添了IPv6、SCTP协议和密钥管理套接字等内容,深入讨论了最新的关键标准、实现和技术。书中的所有示例都是在UNIX系统上测试通过的真实的、可运行的代码,继承了Stevens一直强调的理念:“学习网络编程的最好方法就是下载这些程序,对其进行修改和改进。只有这样实际编写代码才能深入理解有关概念和方法。”读者可以从图灵网站《UNIX网络编程(卷1):套接字联网API(第3版)》网页免费注册下载这些示例的源代码。 《UNIX网络编程(卷1):套接字联网API(第3版)》为UNIX网络编程提供全面的指导,是网络研究和开发人员公认的权威参考书,无论网络编程的初学者还是网络专家都会大受裨益。
Preface 1.Language Processing and Python 1.1 Computing with Language: Texts and Words 1.2 A Closer Look at Python: Texts as Lists of Words 1.3 Computing with Language: Simple Statistics 1.4 Back to Python: Making Decisions and Taking Control 1.5 Automatic Natural Language Understanding 1.6 Summary 1.7 Further Reading 1.8 Exercises
2.Accessing Text Corpora and Lexical Resources 2.1 Accessing Text Corpora 2.2 Conditional Frequency Distributions 2.3 More Python: Reusing Code 2.4 Lexical Resources 2.5 WordNet 2.6 Summary 2.7 Further Reading 2.8 Exercises
3.Processing Raw Text 3.1 Accessing Text from the Web and from Disk 3.2 Strings: Text Processing at the Lowest Level 3.3 Text Processing with Unicode 3.4 Regular Expressions for Detecting Word Patterns 3.5 Useful Applications of Regular Expressions 3.6 Normalizing Text 3.7 Regular Expressions for Tokenizing Text 3.8 Segmentation 3.9 Formatting: From Lists to Strings 3.10 Summary 3.11 Further Reading 3.12 Exercises
4.Writing Structured Programs 4.1 Back to the Basics 4.2 Sequences 4.3 Questions of Style 4.4 Functions: The Foundation of Structured Programming 4.5 Doing More with Functions 4.6 Program Development 4.7 Algorithm Design 4.8 A Sample of Python Libraries 4.9 Summary 4.10 Further Reading 4.11 Exercises
5.Categorizing andTagging Words 5.1 Using a Tagger 5.2 Tagged Corpora 5.3 Mapping Words to Properties Using Python Dictionaries 5.4 Automatic Tagging 5.5 N-Gram Tagging 5.6 Transformation-Based Tagging 5.7 How to Determine the Category of a Word 5.8 Summary 5.9 Further Reading 5.10 Exercises
6.Learning to Classify Text 6.1 Supervised Classification 6.2 Further Examples of Supervised Classification 6.3 Evaluation 6.4 Decision Trees 6.5 Naive Bayes Classifiers 6.6 Maximum Entropy Classifiers 6.7 Modeling Linguistic Patterns 6.8 Summary 6.9 Further Reading 6.10 Exercises
7.Extracting Information from Text 7.1 Information Extraction 7.2 Chunking 7.3 Developing and Evaluating Chunkers 7.4 Recursion in Linguistic Structure 7.5 Named Entity Recognition 7.6 Relation Extraction 7.7 Summary 7.8 Further Reading 7.9 Exercises
8.Analyzing Sentence Structure 8.1 Some Grammatical Dilemmas 8.2 What’s the Use of Syntax? 8.3 Context-Free Grammar 8.4 Parsing with Context-Free Grammar 8.5 Dependencies and Dependency Grammar 8.6 Grammar Development 8.7 Summary 8.8 Further Reading 8.9 Exercises
9.Building Feature-Based Grammars 9.1 Grammatical Features 9.2 Processing Feature Structures 9.3 Extending a Feature-Based Grammar 9.4 Summary 9.5 Further Reading 9.6 Exercises
10.Analyzing the Meaning of Sentences 10.1 Natural Language Understanding 10.2 Propositional Logic 10.3 First-Order Logic 10.4 The Semantics of English Sentences 10.5 Discourse Semantics 10.6 Summary 10.7 Further Reading 10.8 Exercises
11.Managing Linguistic Data 11.1 Corpus Structure: A Case Study 11.2 The Life Cycle of a Corpus 11.3 Acquiring Data 11.4 Working with XML 11.5 Working with Toolbox Data 11.6 Describing Language Resources Using OLAC Metadata 11.7 Summary 11.8 Further Reading 11.9 Exercises Afterword: The Language Challenge Bibliography NLTK Index General Index