Natural Language Processing in Action
Hobson Lane, Cole Howard, Hannes Hapke
  • MEAP began April 2017
  • Publication in Early 2018 (estimated)
  • ISBN 9781617294631
  • 300 pages (estimated)
  • printed in black & white

Natural Language Processing in Action is your guide to creating machines that understand human language. You'll start with a mental model of how a computer learns to read and interpret language. Then, you'll discover how to train a NLP machine to recognize patterns and extract information from text. As you explore the carefully-chosen examples, you'll expand your machine's knowledge and apply it to a range of challenges, from building a search engine that can find documents based on their meaning rather than merely keywords, to training a chatbot that uses deep learning to answer questions and participate in a conversation.

Table of Contents detailed table of contents

Acknowledgments

Part 1: Finding Meaning in Natural Language

1 The Language of Thought

1.1 The Magic

1.2 Practical Applications

1.3 What is Driving NLP Advances?

1.4 Language through a Computer's "Eyes"

1.4.1 A Simple Chatbot

1.4.2 Another Way

1.5 A Brief Overflight of Hyperspace

1.6 Word Order and Grammar

1.7 A Chatbot Natural Language Pipeline

1.8 Processing in Depth

1.9 Natural Language IQ

1.10 Summary

2 Build Your Vocabulary

2.1 Building your vocabulary through tokenization

2.1 A Token Improvement

2.2.1 Contractions

2.3 Extending your vocabulary with N-grams

2.3.1 What are N-grams?

2.3.2 Stopwords

2.4 Normalizing your vocabulary

2.4.1 Case normalization

2.4.2 Stemming

2.4.3 Lemmatization

2.4.4 Use Cases

2.5 Summary

3 Math with Words

3.1 Bag of Words

3.2 Vectorizing

3.3 Zipf's Law

3.4 Topic Modeling

3.4.1 Return of Zipf

3.4.2 Page Rank

3.4.3 Tools

3.5 Summary

Part 2: Your Bot's Summer Reading List

4 Getting Chatty

5 From BOW Vectors to Semantics

6 Word2Vec

7 Disecting Text to Extract Meaning

Part 3 Communicating in the Wild

8 Style and Tone

9 Making Decisions

10 Neural Nets

11 A Sounding Board

12 Hyperparameter Optimization

13 Mind Expansion

14 Review

Appendixes

Appendix A: A Acquiring Words

Appendix B: B You Slice, I Choose

About the Technology

Most humans are pretty good at reading and interpreting text; computers...not so much. Natural Language Processing (NLP) is the discipline of teaching computers to read more like people, and you see examples of it in everything from chatbots to the speech-recognition software on your phone. Modern NLP techniques based on machine learning radically improve the ability of software to recognize patterns, use context to infer meaning, and accurately discern intent from poorly-structured text. NLP promises to help you improve customer interactions, save cost, and reinvent text-intensive applications like search or product support.

What's inside

  • Parsing and normalizing text
  • Rule-based (Grammar) NLP
  • Data-based (Machine Learning) NLP
  • Deep Learning NLP
  • End-to-end chatbot pipeline with training data
  • Scalable NLP pipelines
  • Hyperparameter optimization algorithms

About the reader

A basic understanding of machine learning and some experience with a modern programming language such as Python, Java, C++, or JavaScript will be helpful.

About the authors

Hobson Lane has more than 15 years of experience building autonomous systems that make important decisions on behalf of humans. Hannes Hapke is an Electrical Engineer turned Data Scientist with experience in deep learning. Cole Howard is a carpenter and writer turned Deep Learning expert.


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