Natural Language Processing with Python and spaCy

Table of contents

1. Cover Page
2. Title Page
3. Copyright Page
4. About the Authors
5. About the Technical Reviewer
6. BRIEF CONTENTS
7. CONTENTS IN DETAIL
8. INTRODUCTION
   1. Using Python for Natural Language Processing
   2. The spaCy Library
   3. Who Should Read This Book?
   4. What’s in the Book?
9. 1 HOW NATURAL LANGUAGE PROCESSING WORKS
   1. How Can Computers Understand Language?
   2. What Is a Statistical Model in NLP?
   3. What Is Still on You
   4. Summary
10. 2 THE TEXT-PROCESSING PIPELINE
    1. Setting Up Your Working Environment
    2. Installing Statistical Models for spaCy
    3. Basic NLP Operations with spaCy
    4. Summary
11. 3 WORKING WITH CONTAINER OBJECTS AND CUSTOMIZING SPACY
    1. spaCy’s Container Objects
    2. Customizing the Text-Processing Pipeline
    3. Using spaCy’s C-Level Data Structures
    4. Summary
12. 4 EXTRACTING AND USING LINGUISTIC FEATURES
    1. Extracting and Generating Text with Part-of-Speech Tags
    2. Using Syntactic Dependency Labels in Text Processing
    3. Summary
13. 5 WORKING WITH WORD VECTORS
    1. Understanding Word Vectors
    2. Installing Word Vectors
    3. Comparing spaCy Objects
    4. Summary
14. 6 FINDING PATTERNS AND WALKING DEPENDENCY TREES
    1. Word Sequence Patterns
    2. Extracting Keywords from Syntactic Dependency Trees
    3. Using Context to Improve the Ticket-Booking Chatbot
    4. Making a Smarter Chatbot by Finding Proper Modifiers
    5. Summary
15. 7 VISUALIZATIONS
    1. Getting Started with spaCy’s Built-In Visualizers
    2. Visualizing from Within spaCy
    3. Customizing Your Visualizations with the Options Argument
    4. Exporting a Visualization to a File
    5. Using displaCy to Manually Render Data
    6. Summary
16. 8 INTENT RECOGNITION
    1. Extracting the Transitive Verb and Direct Object for Intent Recognition
    2. Finding the Meanings of Words Using Synonyms and Semantic Similarity
    3. Extracting Intent from a Sequence of Sentences
    4. Summary
17. 9 STORING USER INPUT IN A DATABASE
    1. Converting Unstructured Data into Structured Data
    2. Building a Database-Powered Chatbot
    3. Summary
18. 10 TRAINING MODELS
    1. Training a Model’s Pipeline Component
    2. Training the Entity Recognizer
    3. Creating a New Dependency Parser
    4. Summary
19. 11 DEPLOYING YOUR OWN CHATBOT
    1. How Implementing and Deploying a Chatbot Works
    2. Using Telegram as a Platform for Your Bot
    3. Summary
20. 12 IMPLEMENTING WEB DATA AND PROCESSING IMAGES
    1. How It Works
    2. Making Your Bot Find Answers to Questions from Wikipedia
    3. Reacting to Images Sent in a Chat
    4. Putting All the Pieces Together in a Telegram Bot
    5. Summary
21. LINGUISTIC PRIMER
    1. Dependency Grammars vs. Phrase Structure Grammars
    2. Common Grammar Concepts
22. INDEX