Python自然語言處理（影印版）
本書提供了非常易學的自然語言處理入門介紹，該領域涵蓋從文本和電子郵件預測過濾，到自動總結和翻譯等多種語言處理技術。在本書中，你將學會編寫Python程序處理大量非結構化文本。你還將通過使用綜合語言數據結構...
本書提供了非常易學的自然語言處理入門介紹，該領域涵蓋從文本和電子郵件預測過濾，到自動總結和翻譯等多種語言處理技術。在本書中，你將學會編寫Python程序處理大量非結構化文本。你還將通過使用綜合語言數據結構訪問含有豐富注釋的數據集，理解用于分析書面通信內容和結構的主要算法。
本書提供了非常易學的自然語言處理入門介紹，該領域涵蓋從文本和電子郵件預測過濾，到自動總結和翻譯等多種語言處理技術。在本書中，你將學會編寫Python程序處理大量非結構化文本。你還將通過使用綜合語言數據結構訪問含有豐富注釋的數據集，理解用于分析書面通信內容和結構的主要算法。
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
價格還可以，書質量不錯
結合 NPL那個庫，
還沒看呢，應該很好很強大
沒有中文版的嗎？
好評
python深入學習用書，還沒看聽說寫的還可以
先介紹下自身的情況，本人是國內某前三計算機專業的研究生，方向是信息檢索，當然主要是基于機器學習和數據挖掘的一些方法。這本書應該說不需要任何的背景，當然有一些編程的基礎更好，也不要求有機器學習的背景，有則更加。因為這兩個背景我都有，因此花了大概五六天的時間閱讀了一半，此刻我來寫評語。這門書的結構很清楚，尤其是第一章介紹了自然語言處理的pipeline，接下來的chapter都緊扣這個pipeline，如果沒有python的基礎建議跟著進度敲一下代碼，如果覺得pyhon自帶的ide不方便，個人推薦用ulipad，有自動代碼提示的功能不用按tab鍵了。從第六章開始有learnin…
自然語言處理的實戰之書，內容不深，但相對比較實用。
對語料庫感興趣，聽程序員朋友說過Python處理字符串特別方便。這是一本介紹如何利用Python進行自然語言處理的專業圖書，但非常好讀。Python編程知識和技巧、自然語言處理基本概念和方法（如分詞、標注、句法分析等等）盡在此書。特別適用對自然語言處理有所了解，但對編程還不太摸門的讀者。
