Audiobook description
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, where she is part of the AI research group.
Quotes
An accessible entry point. Learn key NLP concepts by building real-world projects.
- Samantha Berk, AdaptX
A well-written, pragmatic book.
- James Richard Woodruff, SAIC
The best NLP resource.
- Najeeb Arif, ThoughtWorks
Get started with NLP and understand its fundamentals.
- Walter Alexander Mata López, University of Colima
Makes a difficult subject easy to understand.
- Tanya Wilke, .NET Engineer
Publisher resources
Table of contents
- Chapter 1. Introduction
- Chapter 2. Your first NLP example
- Chapter 3. Introduction to information search
- Chapter 4. Information extraction
- Chapter 5. Author profiling as a machine-learning task
- Chapter 6. Linguistic feature engineering for author profiling
- Chapter 7. Your first sentiment analyzer using sentiment lexicons
- Chapter 8. Sentiment analysis with a data-driven approach
- Chapter 9. Topic analysis
- Chapter 10. Topic modeling
- Chapter 11. Named-entity recognition
Product information
- Title: Getting Started with Natural Language Processing
- Author(s):
- Release date: October 2022
- Publisher(s): Manning Publications
- ISBN: None
You might also like
audiobook
Natural Language Processing in Action
"Learn both the theory and practical skills needed to go beyond merely understanding the inner workings …
book
Natural Language Processing with Transformers, Revised Edition
Since their introduction in 2017, transformers have quickly become the dominant architecture for achieving state-of-the-art results …
video
Learning Deep Learning: From Perceptron to Large Language Models
13+ Hours of Video Instruction A complete guide to deep learning for artificial intelligence Deep learning …
video
Introduction to Transformer Models for NLP: Using BERT, GPT, and More to Solve Modern Natural Language Processing Tasks
10+ Hours of Video Instruction Learn how to apply state-of-the-art transformer-based models including BERT and GPT …