书名
Python自然语言处理封面
Python自然语言处理内容简介
《Python自然言语处理(影印版)》供给了十分易学的自然言语处理入门介绍，该领域包括从文本和电子邮件猜想过滤，到主动总结和翻译等多种言语处理技术。在《Python自然言语处理(影印版)》中，你将学会编写Python程序处理大量非结构化文本。你还将经过使用综合言语数据结构访问含有丰富注释的数据集，理解用于剖析书面通信内容和结构的主要算法。
《Python自然言语处理》预备了充足的示例和练习，可以帮助你：
从非结构化文本中抽取信息，甚至猜想主题或识别“命名实体”；
剖析文本言语结构，包括解析和语义剖析；
访问盛行的言语学数据库，包括WordNet和树库(treebank)；
从多种言语学和人工智能领域中提取的整合技巧。
《Python自然言语处理(影印版)》将帮助你学习运用Python编程言语和自然言语工具包(NLTK)取得实用的自然言语处理技术。如果关于开发Web应用、剖析多言语新闻源或记录濒危言语感兴趣——即使仅仅想从程序员视角观察人类言语如何运作，你将发现《Python自然言语处理》是一本令人入神且极为有用的好书。
Python自然语言处理作者简介
Python自然语言处理目录
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 Whats 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
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