Real-World Natural Language Processing
Masato Hagiwara
  • MEAP began July 2019
  • Publication in Spring 2020 (estimated)
  • ISBN 9781617296420
  • 500 pages (estimated)
  • printed in black & white
Voice assistants, automated customer service agents, and other cutting-edge human-to-computer interactions rely on accurately interpreting language as it is written and spoken. Real-world Natural Language Processing teaches you how to create practical NLP applications without getting bogged down in complex language theory and the mathematics of deep learning. In this engaging book, you’ll explore the core tools and techniques required to build a huge range of powerful NLP apps.
Table of Contents detailed table of contents

Part 1: Basics

1 Introduction to Natural Language Processing

1.1 What is natural language processing (NLP)?

1.1.1 What is NLP

1.1.2 What is not NLP

1.1.3 AI, ML, DL, and NLP

1.1.4 Why NLP?

1.2 How NLP is used

1.2.1 NLP applications

1.2.2 NLP tasks

1.3 Building NLP applications

1.3.1 Development of NLP applications

1.3.2 Structure of NLP applications

1.4 Summary

2 Your First NLP Application

2.1 Introduction to sentiment analysis

2.2 Working with NLP datasets

2.2.1 What is a dataset?

2.2.2 Stanford Sentiment Treebank

2.2.3 Train, validation, and test sets

2.2.4 Loading SST datasets using AllenNLP

2.3 Using word embeddings

2.3.1 What word embeddings are

2.3.2 How to use word embeddings for sentiment analysis

2.4 Neural networks

2.4.1 What neural networks are

2.4.2 Recurrent neural networks (RNNs) and linear layers

2.4.3 Architecture for sentiment analysis

2.5 Loss functions and optimization

2.6 Training your own classifier

2.6.1 Batching

2.6.2 Putting everything together

2.7 Evaluating your classifier

2.8 Deploying your application

2.8.1 Making predictions

2.8.2 Serving predictions

2.9 Summary

3 Word and Document Embeddings

3.1 Introduction to embeddings

3.1.1 What embeddings are

3.1.2 Why embeddings are important

3.2 Building blocks of language: characters, words, and phrases

3.2.1 Characters

3.2.2 Words, tokens, morphemes, and phrases

3.2.3 N-grams

3.3 Tokenization, stemming, and lemmatization

3.3.1 Tokenization

3.3.2 Stemming

3.3.3 Lemmatization

3.4 Skip-gram and continuous bag-of-words (CBOW)

3.4.1 Where word embeddings come from

3.4.2 Using word associations

3.4.3 Linear layers

3.4.4 Softmax

3.4.5 Implementing Skip-gram on AllenNLP

3.4.6 Continuous bag-of-words (CBOW) model

3.5 GloVe

3.5.1 How GloVe learns word embeddings

3.5.2 Using pre-trained GloVe vectors

3.6 FastText

3.6.1 Making use of subword information

3.6.2 Using FastText toolkit

3.7 Document-level embeddings

3.8 Visualizing embeddings

3.9 Summary

4 Sentence classification

5 Sequential labeling and Language Modeling

Part 2: Advanced Models

6 Sequence to sequence models

7 Convolutional neural networks

8 Memory and attention

9 Transfer and multitask learning

Part 3: Putting into Production

10 Best practices in developing NLP application

11 Deploying and serving NLP application

About the Technology

Natural language processing is the part of AI dedicated to understanding and generating human text and speech. NLP covers a wide range of algorithms and tasks, from classic functions such as spell checkers, machine translation, and search engines to emerging innovations like chatbots, voice assistants, and automatic text summarization. Wherever there is text, NLP can be useful for extracting meaning and bridging the gap between humans and machines.

About the book

Real-world Natural Language Processing teaches you how to create practical NLP applications using Python and open source NLP libraries such as AllenNLP and Fairseq. In this practical guide, you’ll begin by creating a complete sentiment analyzer, then dive deep into each component to unlock the building blocks you’ll use in all different kinds of NLP programs. By the time you’re done, you’ll have the skills to create named entity taggers, machine translation systems, spelling correctors, and language generation systems.

What's inside

  • Design, develop, and deploy basic NLP applications
  • NLP libraries such as AllenNLP and Fairseq
  • Advanced NLP concepts such as attention and transfer learning

About the reader

Aimed at intermediate Python programmers. No mathematical or machine learning knowledge required.

About the author

Masato Hagiwara received his computer science PhD from Nagoya University in 2009, focusing on Natural Language Processing and machine learning. He has interned at Google and Microsoft Research, and worked at Baidu Japan, Duolingo, and Rakuten Institute of Technology. He now runs his own consultancy business advising clients, including startups and research institutions.

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