Getting Started with Natural Language Processing, Ekaterina Kochmar

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.

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

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