Natural Language Processing with Python Review

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Natural Language Processing with Python ReviewThis book really delivers when it comes to code. It starts with simple tasks using the Python NLTK (Natural Language Toolkit) and builds up from there, teaching you a little bit of Python, a little bit of NLP theory, and delivering much in the way of useful applications. The author takes the time to explain the code and what is going on behind the scenes. He starts with extracting explicit words from documents and builds on that until at the end of the book you are analyzing sentence structure and building feature-based grammars.
This is not, however, an introduction to either the mathematics or information theory of natural language processing. It is not even a tutorial on Python. The book's sole purpose is to help you solve real problems using a common language without necessarily understanding the theory or the language you are using. If you really want to understand Python I suggest Learning Python. It's not as interestng as this book, but it gets the job done. To understand the theory behind natural language processing and also see how algorithms are coded up I suggest An Introduction to Language Processing with Perl and Prolog: An Outline of Theories, Implementation, and Application with Special Consideration of English, French, and German (Cognitive Technologies).
As for this book, I think it makes a great supplement to the other books I mention and also as a recipe book of solutions to real-world problems. I really don't think it is a gentle introduction to Speech and Language Processing (2nd Edition) (Prentice Hall Series in Artificial Intelligence), as it claims to be in the preface. Currently the table of contents is not listed in the product description. I include that next for your convenience:

Chapter 1. Language Processing and Python
Section 1.1. Computing with Language: Texts and Words
Section 1.2. A Closer Look at Python: Texts as Lists of Words
Section 1.3. Computing with Language: Simple Statistics
Section 1.4. Back to Python: Making Decisions and Taking Control
Section 1.5. Automatic Natural Language Understanding
Section 1.6. Summary
Section 1.7. Further Reading
Section 1.8. Exercises
Chapter 2. Accessing Text Corpora and Lexical Resources
Section 2.1. Accessing Text Corpora
Section 2.2. Conditional Frequency Distributions
Section 2.3. More Python: Reusing Code
Section 2.4. Lexical Resources
Section 2.5. WordNet
Section 2.6. Summary
Section 2.7. Further Reading
Section 2.8. Exercises
Chapter 3. Processing Raw Text
Section 3.1. Accessing Text from the Web and from Disk
Section 3.2. Strings: Text Processing at the Lowest Level
Section 3.3. Text Processing with Unicode
Section 3.4. Regular Expressions for Detecting Word Patterns
Section 3.5. Useful Applications of Regular Expressions
Section 3.6. Normalizing Text
Section 3.7. Regular Expressions for Tokenizing Text
Section 3.8. Segmentation
Section 3.9. Formatting: From Lists to Strings
Section 3.10. Summary
Section 3.11. Further Reading
Section 3.12. Exercises
Chapter 4. Writing Structured Programs
Section 4.1. Back to the Basics
Section 4.2. Sequences
Section 4.3. Questions of Style
Section 4.4. Functions: The Foundation of Structured Programming
Section 4.5. Doing More with Functions
Section 4.6. Program Development
Section 4.7. Algorithm Design
Section 4.8. A Sample of Python Libraries
Section 4.9. Summary
Section 4.10. Further Reading
Section 4.11. Exercises
Chapter 5. Categorizing and Tagging Words
Section 5.1. Using a Tagger
Section 5.2. Tagged Corpora
Section 5.3. Mapping Words to Properties Using Python Dictionaries
Section 5.4. Automatic Tagging
Section 5.5. N-Gram Tagging
Section 5.6. Transformation-Based Tagging
Section 5.7. How to Determine the Category of a Word
Section 5.8. Summary
Section 5.9. Further Reading
Section 5.10. Exercises
Chapter 6. Learning to Classify Text
Section 6.1. Supervised Classification
Section 6.2. Further Examples of Supervised Classification
Section 6.3. Evaluation
Section 6.4. Decision Trees
Section 6.5. Naive Bayes Classifiers
Section 6.6. Maximum Entropy Classifiers
Section 6.7. Modeling Linguistic Patterns
Section 6.8. Summary
Section 6.9. Further Reading
Section 6.10. Exercises
Chapter 7. Extracting Information from Text
Section 7.1. Information Extraction
Section 7.2. Chunking
Section 7.3. Developing and Evaluating Chunkers
Section 7.4. Recursion in Linguistic Structure
Section 7.5. Named Entity Recognition
Section 7.6. Relation Extraction
Section 7.7. Summary
Section 7.8. Further Reading
Section 7.9. Exercises
Chapter 8. Analyzing Sentence Structure
Section 8.1. Some Grammatical Dilemmas
Section 8.2. What's the Use of Syntax?
Section 8.3. Context-Free Grammar
Section 8.4. Parsing with Context-Free Grammar
Section 8.5. Dependencies and Dependency Grammar
Section 8.6. Grammar Development
Section 8.7. Summary
Section 8.8. Further Reading
Section 8.9. Exercises
Chapter 9. Building Feature-Based Grammars
Section 9.1. Grammatical Features
Section 9.2. Processing Feature Structures
Section 9.3. Extending a Feature-Based Grammar
Section 9.4. Summary
Section 9.5. Further Reading
Section 9.6. Exercises
Chapter 10. Analyzing the Meaning of Sentences
Section 10.1. Natural Language Understanding
Section 10.2. Propositional Logic
Section 10.3. First-Order Logic
Section 10.4. The Semantics of English Sentences
Section 10.5. Discourse Semantics
Section 10.6. Summary
Section 10.7. Further Reading
Section 10.8. Exercises
Chapter 11. Managing Linguistic Data
Section 11.1. Corpus Structure: A Case Study
Section 11.2. The Life Cycle of a Corpus
Section 11.3. Acquiring Data
Section 11.4. Working with XML
Section 11.5. Working with Toolbox Data
Section 11.6. Describing Language Resources Using OLAC Metadata
Section 11.7. Summary
Section 11.8. Further Reading
Section 11.9. ExercisesNatural Language Processing with Python Overview
This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation. With it, you'll learn how to write Python programs that work with large collections of unstructured text. You'll access richly annotated datasets using a comprehensive range of linguistic data structures, and you'll understand the main algorithms for analyzing the content and structure of written communication. Packed with examples and exercises, Natural Language Processing with Python will help you:

Extract information from unstructured text, either to guess the topic or identify "named entities"
Analyze linguistic structure in text, including parsing and semantic analysis
Access popular linguistic databases, including WordNet and treebanks
Integrate techniques drawn from fields as diverse as linguistics and artificial intelligence
This book will help you gain practical skills in natural language processing using the Python programming language and the Natural Language Toolkit (NLTK) open source library. If you're interested in developing web applications, analyzing multilingual news sources, or documenting endangered languages -- or if you're simply curious to have a programmer's perspective on how human language works -- you'll find Natural Language Processing with Python both fascinating and immensely useful.

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