Natural.Language.Processing.with.Python

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Natural Language Processing with Python,一本不错的学习nlp和phthon的书。英文版,但简单易懂。刚刚得到,分享给大家。
Natural Language Processing with Python Natural Language processing with Python Steven bird, Ewan Klein and Edward loper ○ REILLY Beijing· Cambridge· Farnham:Kon: Sebastopol· alper· Tokyo Natural Language Processing with Python by Steven Bird, Ewan Klein, and Edward Loper Copyright C 2009 Steven Bird, Ewan Klein, and Edward Loper. All rights reserved Printed in the United States of america Published by O'Reilly Media, Inc, 1005 Gravenstein Highway North, Sebastopol, CA 95472 O' Reilly books may be purchased for educational, business, or sales promotional use Online editions arealsoavailableformosttitles(http://my.safaribooksonline.com).Formoreinformationcontactour corporate/institutionalsalesdepartment(800)998-9938orcorporate@oreilly.com Editor: Julie Steele Indexer: Ellen Troutman Zaig Production editor: Loranah Dimant Cover Designer: Karen Montgomery Copyeditor: Genevieve d'Entremont Interior Designer: David Futato Proofreader: Loranah Dimant Illustrator: Robert romano Printing History June 2009 First edition Nutshell Handbook, the Nutshell Handbook logo, and the O Reilly logo are registered trademarks of O'Reilly Media, Inc. Natural Language Processing with Python, the image of a right whale, and related trade dress are trademarks of o' reilly media, Inc Many of the designations used by manufacturers and sellers to distinguish their products are claimed as trademarks. Where those designations appear in this book, and O'Reilly Media, Inc. was aware of a trademark claim, the designations have been printed in caps or initial caps While every precaution has been taken in the preparation of this book, the publisher and authors assume no responsibility for errors or omissions, or for damages resulting from the use of the information con- tained herein ISBN:978-0-596-51649-9 [MI 1244726609 Table of contents 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 22 1. 5 Automatic Natural Language understanding 1.6 Summary 33 1.7 Further reading 34 1. 8 Exercises 35 2. Accessing Text Corpora and lexical resources 39 2.1 Accessing Text Corpora 39 2.2 Conditional Frequency Distributions 52 2.3 More Python: Reusing Code 56 2.4 Lexical resources 59 2.5 WordNet 67 2.6 Summary 2.7 Further Reading 73 2. 8 Exercises 74 3. Processing raw Text 3. 1 Accessing Text from the Web and from Disk 80 3.2 Strings: Text Processing at the Lowest level 3.3 Text Processing with Unicode 93 3.4 Regular Expressions for Detecting Word Patterns 3.5 Useful Applications of Regular Expressions 102 3.6 Normalizing Text 107 3.7 Regular Expressions for Tokenizing Text 109 3.8 Segmentation 112 3.9 Formatting: From Lists to Strings 116 3.10 Summary 121 3. 11 Further reading 122 3.12 Exercises 4. Writing Structured Programs ,,129 4.1 Back to the basics 130 4.2 Sequences 133 4.3 Questions of Style 138 4.4 Functions: The Foundation of Structured Programming 142 4.5 Doing more with Functions 149 4.6 Program Development 154 4.7 Algorithm Design 160 4.8 A Sample of Python Libraries 167 4.9 Summary 172 4.10 Further Reading 173 4.11 Exercises 173 5.〔 categorizing and Tagging Words∴… 179 5.1U ng a l agger 179 5.2 Tagged Corpora 181 5.3 Mapping words to Properties using python dictionaries 189 5.4 Automatic Tagging 198 5.5 N-Gram Tagging 202 5.6 Transformation-Based Tagging 208 5.7 How to Determine the Category of a Word 210 5. 8 Summary 213 5. 9 Further Reading 214 5.10 Exercises 215 6. Learning to Classify Text.......... 221 6.1 Supervised classification 221 6.2 Further Examples of Supervised classification 233 6.3 Evaluation 237 6. 4 Decision trees 242 6.5 Naive Bayes Classifiers 245 6.6 Maximum Entropy Classifiers 250 6.7 Modeling linguistic Patterns 254 6. 8 Summary 256 6.9 Further reading 256 6.10 Exercises 257 7. Extracting Information from Text ,,261 7. 1 Information Extraction 261 I Table of Contents 7.2 Chunking 264 7.3 Developing and evaluating Chunkers 270 7.4 Recursion in linguistic structure 277 7.5 Named Entity recognition 281 7. 6 Relation extraction 284 7.7 Summary 285 7. 8 Further reading 286 7.9 Exercises 286 8. Analyzing Sentence Structure 291 8. 1 Some grammatical dilemmas 292 8.2 What's the Use of Syntax? 295 8. 3 Context-Free grammar 298 8.4 Parsing with Context-Free Grammar 302 8.5 Dependencies and Dependency grammar 310 8.6 Grammar Development 315 8.7 Summary 321 8. 8 Further reading 322 8.9 Exercises 322 9. Building Feature- Based grammars..……327 9.1 Grammatical features 327 9.2 Processing Feature Structures 337 9.3 Extending a Feature-Based grammar 344 9. 4 Summary 356 9.5 Further readin g 357 9.6 Exercises 358 10. Analyzing the Meaning of Sentences .,..... ............ 361 10. 1 Natural Language Understanding 361 10.2 Propositional logic 368 10.3 First-Order logic 372 10.4 The Semantics of English Sentences 385 10.5 Discourse Semantics 397 10.6 Summar 402 10.7 Further reading 403 10.8 Exercises 404 11. Managing linguistic Data............ 11.1 Corpus Structure: A Case Stud 407 11.2 The Life Cycle of a Corpus 412 11.3 Acquiring Data 416 11.4 Working with XML 425 Table of contents|ⅶi 11.5 Working with Toolbox Dat 431 11.6 Describing Language Resources Using OLAC Metadata 435 11.7 Summar 437 11. 8 Further Reading 437 11.9 Exercises 438 Afterword: The language challenge...................... 441 Bibliography 449 nLTK Index 459 General index ⅶ ii Table of Contents

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technicaec7 正准备用python+tensorflow做聊天机器人
2017-10-19
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richyrichy This is a good idea to learn natural language processing through practical Python coding.
2017-08-07
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