Getting Started with Natural Language Processing: A friendly introduction using Python

Hit the ground running with this in-depth introduction to the NLP skills and techniques that allow your computers to speak human.

In Getting Started with Natural Language Processing you'll learn about:

    Fundamental concepts and algorithms of NLP
    Useful Python libraries for NLP
    Building a search algorithm
    Extracting information from raw text
    Predicting sentiment of an input text
    Author profiling
    Topic labeling
    Named entity recognition

Getting Started with Natural Language Processing is an enjoyable and understandable guide that helps you engineer your first NLP algorithms. Your tutor is Dr. Ekaterina Kochmar, lecturer at the University of Bath, who has helped thousands of students take their first steps with NLP. Full of Python code and hands-on projects, each chapter provides a concrete example with practical techniques that you can put into practice right away. If you're a beginner to NLP and want to upgrade your applications with functions and features like information extraction, user profiling, and automatic topic labeling, this is the book for you.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the technology
From smart speakers to customer service chatbots, apps that understand text and speech are everywhere. Natural language processing, or NLP, is the key to this powerful form of human/computer interaction. And a new generation of tools and techniques make it easier than ever to get started with NLP!

About the book
Getting Started with Natural Language Processing teaches you how to upgrade user-facing applications with text and speech-based features. From the accessible explanations and hands-on examples in this book you'll learn how to apply NLP to sentiment analysis, user profiling, and much more. As you go, each new project builds on what you've previously learned, introducing new concepts and skills. Handy diagrams and intuitive Python code samples make it easy to get started—even if you have no background in machine learning!

What's inside

    Fundamental concepts and algorithms of NLP
    Extracting information from raw text
    Useful Python libraries
    Topic labeling
    Building a search algorithm

About the reader
You'll need basic Python skills. No experience with NLP required.

About the author
Ekaterina Kochmar is a lecturer at the Department of Computer Science of the University of Bath and a cofounder and the chief scientific officer of Korbit AI.

Table of Contents
1 Introduction
2 Your first NLP example
3 Introduction to information search
4 Information extraction
5 Author profiling as a machine-learning task
6 Linguistic feature engineering for author profiling
7 Your first sentiment analyzer using sentiment lexicons
8 Sentiment analysis with a data-driven approach
9 Topic analysis
10 Topic modeling
11 Named-entity recognition

Ekaterina Kochmar is a lecturer (assistant professor) at the Department of Computer Science of the University of Bath, where she is part of the AI research group. Her research lies at the intersection of artificial intelligence, natural language processing, and intelligent tutoring systems. She holds a PhD in natural language processing, an MPhil in advanced computer science from the University of Cambridge, and an MA in computational linguistics from the University of Tuebingen. She is also a cofounder and the chief scientific officer of Korbit AI, focusing on building an AI-powered dialogue-based tutoring system capable of providing learners with high-quality, interactive, and personalized education. Ekaterina has extensive experience in teaching both within and outside of academia.