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商品簡介
作者簡介
目次
商品簡介
《Python自然語言處理(影印版)》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《Python自然語言處理(影印版)》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。
《Python自然語言處理》準備了充足的示例和練習，可以幫助你：
從非結構化文本中抽取信息，甚至猜測主題或識別“命名實體”；
分析文本語言結構，包括解析和語義分析；
訪問流行的語言學數據庫，包括WordNet和樹庫(treebank)；
從多種語言學和人工智能領域中提取的整合技巧。
《Python自然語言處理(影印版)》將幫助你學習運用Python編程語言和自然語言工具包(NLTK)獲得實用的自然語言處理技能。如果對于開發Web應用、分析多語言新聞源或記錄瀕危語言感興趣——即便只是想從程序員視角觀察人類語言如何運作，你將發現《Python自然語言處理》是一本令人著迷且極為有用的好書。
《Python自然語言處理》準備了充足的示例和練習，可以幫助你：
從非結構化文本中抽取信息，甚至猜測主題或識別“命名實體”；
分析文本語言結構，包括解析和語義分析；
訪問流行的語言學數據庫，包括WordNet和樹庫(treebank)；
從多種語言學和人工智能領域中提取的整合技巧。
《Python自然語言處理(影印版)》將幫助你學習運用Python編程語言和自然語言工具包(NLTK)獲得實用的自然語言處理技能。如果對于開發Web應用、分析多語言新聞源或記錄瀕危語言感興趣——即便只是想從程序員視角觀察人類語言如何運作，你將發現《Python自然語言處理》是一本令人著迷且極為有用的好書。
作者簡介
作者：（英國）伯德（Steven Bird） （英國）克萊因（Ewan Klein） （美國）洛普（Edward Loper）
伯德（Steven Bird）是墨爾本大學計算機科學和軟件工程系副教授，以及賓夕法尼亞大學語言數據聯合會高級研究助理。
克萊因（Ewan Klein）是愛丁堡大學信息學院語言技術教授。
洛普（Edward Loper）最近從賓夕法尼亞大學獲得機器學習自然語言處理博士學位，目前是波士頓BBN Technologies公司的研究員。
伯德（Steven Bird）是墨爾本大學計算機科學和軟件工程系副教授，以及賓夕法尼亞大學語言數據聯合會高級研究助理。
克萊因（Ewan Klein）是愛丁堡大學信息學院語言技術教授。
洛普（Edward Loper）最近從賓夕法尼亞大學獲得機器學習自然語言處理博士學位，目前是波士頓BBN Technologies公司的研究員。
目次
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
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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