Natural Language Processing with Python and SpaCy : A Practical Introduction

An introduction to natural language processing with Python using spaCy, a leading Python natural language processing library.Natural Language Processing with Python and spaCy will show you how to create NLP applications like chatbots, text-condensing scripts, and order-processing tools quickly and easily. You'll learn how to leverage the spaCy library to extract meaning from text intelligently; how to determine the relationships between words in a sentence (syntactic dependency parsing); identify nouns, verbs, and other parts of speech (part-of-speech tagging); and sort proper nouns into categories like people, organizations, and locations (named entity recognizing). You'll even learn how to transform statements into questions to keep a conversation going.You'll also learn how to: • Work with word vectors to mathematically find words with similar meanings (Chapter 5) • Identify patterns within data using spaCy's built-in displaCy visualizer (Chapter 7) • Automatically extract keywords from user input and store them in a relational database (Chapter 9) • Deploy a chatbot app to interact with users over the internet (Chapter 11)'Try This'sections in each chapter encourage you to practice what you've learned by expanding the book's example scripts to handle a wider range of inputs, add error handling, and build professional-quality applications.By the end of the book, you'll be creating your own NLP applications with Python and spaCy. 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 An Introduction To Natural Language Processing With Python Using Spacy, A Leading Python Natural Language Processing Library. Natural Language Processing Using Python Is An Introduction To Natural Language Processing (nlp), The Task Of Converting Human Language Into Data That A Computer Can Process. The Book Uses Spacy, A Leading Python Library For Nlp, To Guide Readers Through Common Nlp Tasks Related To Generating And Understanding Human Language With Code. It Addresses Problems Like Understanding A User's Intent, Continuing A Conversation With A Human, And Maintaining The State Of A Conversation. It Also Teaches You How To Connect An Nlp Script To A Messaging App, Store User Data In A Database To Fill In A Form, And Customize Your Own Statistical Models To Improve Text Processing. This Book Will Give You The Foundation You Need To Build Your Own Chatbots, Ticket-ordering Apps, Text-condensing Scripts, And More. "Teaches the foundations of natural language processing (the task of converting human language into data a computer can process), including how to understand a user's intent, customize a statistical model, and set up a basic chatbot"-- Provided by publisher