贾拉·萨拉基著的《Python自然语言处理(影印版)(英文版)》首先阐述了自然语言处理（Natural Language Processing，NLP）的基础，以及为什么Python是构建基于NLP的专家系统的最佳选择之一，其中包括社区支持和可用框架等优势。它还能够使你更好地理解可用的免费语料库以及不同类型的数据集。随后，你会学到如何为NLP应用选择数据集，找到正确的NLP技术来处理数据集中的句子并理解其结构。另外还将学习如何标记句子的不同部分并查看其分析方法。在阅读本书的过程中，你将探索文本的语义和句法分析。了解如何解决处理人类语言时出现的各种歧义，碰到在执行文本分析时出现各种情况。你会学到设置NLP环境的基础知识，初始化设置，然后快速理解句子和语言。你将领会到利用机器学习和深度学习从文本数据中提取信息的威力。在本书的结尾，你会对NLP有一个清晰的理解并在现实中实现多个NLP示例。

Preface
Chapter 1：Introduction
Understanding natural language processing
Understanding basic applications
Understanding advanced applications
Advantages of togetherness—N LP and Python
Environment setup for NLTK
Tips for readers
Summary
Chapter 2：Practical Understanding of a Corpus and Datase
What is a corpus?
Why do we need a corpus?
UnderStanding corpus analysis
Exercise
Understanding types of data attributes
Categorical or qualitative data attributes
Numeric or quantitative data attributes
Exploring different file formats for corpora
Resources for accessing free corpora
Preparing a dataset for NLP applications
Selecting data
Preprocessing the dataset
Formatting
Cleaning
Sampling
Transforming data
Web scraping
Summary
Chapter 3：Understanding the Structure of a Sentences
Understanding components of NLP
Natural language understanding
Natural language generation
Differences between NLU and NLG
Branches nf NLP
Defining context-free grammar
Exercise
Morphological analysis
What is morphology?
What are morphemes?
What is a stem?
What is morphological analysis?
What iS a word?
Classification of morphemes
Free morphemes
Bound morphemes
Derivational morphemes
Inflectional morphemes
What is the difference between a stem and a root?
Exercise
Lexical analysis
Whal is a token?
What are part of speech tags?
Process of deriving tokens
Difference between stemming and lemmatization
Applications
Syntactic analysis
What is syntactic analysis?
Semantic analysis
What is semantic analysis?
Lexical semantics
Hyponymy and hyponyms
Homonymy
Polysemy
What is the difference between polysemy and homonymy?
Application of semantic analysis
Handling ambiguity
Lexical ambiguity
Syntactic ambiguity
Approach to handle syntactic ambiguity
Semantic ambiguity
Pragmatic ambiguity
Discourse integration
Applications
Pragmatic analysis
Summary
Chapter 4: PreproceSSing
Chapter 5: Feature Engineering and NLP Alclorithms
Chapter 6：Advanced Feature Engineering and NLP Algorithms
Chapter 7: Rule-Based System for NLP
Chapter 8: Machine Learning for NLP Problems
Chapter 9: Deep Learnincl for NLU and NLG Problems
Chapter 10: Advanced Tools
Chapter 11 : How to Improve Your NLP Skills
Chapter 12: Installation Guide
Index