Natural Language Processing with Python and Spacy: A Practical Introduction

✍ Scribed by Yuli Vasiliev

• Publisher: No Starch Press
• Year: 2020
• Tongue: English
• Leaves: 268
• Category: Library

✦ Table of Contents

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