Real-world Natural Language Processing

shows you how to build the practical NLP applications that are transforming the way humans and computers work together.

In

Real-world Natural Language Processing

you will learn how to:

Design, develop, and deploy useful NLP applications

Create named entity taggers

Build machine translation systems

Construct language generation systems and chatbots

Use advanced NLP concepts such as attention and transfer learning

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, including chatbots, language detectors, and text classifiers.

About the technology

Training computers to interpret and generate speech and text is a monumental challenge, and the payoff for reducing labor and improving human/computer interaction is huge! Th e field of Natural Language Processing (NLP) is advancing rapidly, with countless new tools and practices. This unique book offers an innovative collection of NLP techniques with applications in machine translation, voice assistants, text generation, and more.

About the book

Real-world Natural Language Processing

shows you how to build the practical NLP applications that are transforming the way humans and computers work together. Guided by clear explanations of each core NLP topic, you’ll create many interesting applications including a sentiment analyzer and a chatbot. Along the way, you’ll use Python and open source libraries like AllenNLP and HuggingFace Transformers to speed up your development process.

What's inside

Design, develop, and deploy useful NLP applications

Create named entity taggers

Build machine translation systems

Construct language generation systems and chatbots

About the reader

For Python programmers. No prior machine learning knowledge assumed.

About the author

Masato Hagiwara

received his computer science PhD from Nagoya University in 2009. He has interned at Google and Microsoft Research, and worked at Duolingo as a Senior Machine Learning Engineer. He now runs his own research and consulting company.

Table of Contents

PART 1 BASICS

1 Introduction to natural language processing

2 Your first NLP application

3 Word and document embeddings

4 Sentence classification

5 Sequential labeling and language modeling

PART 2 ADVANCED MODELS

6 Sequence-to-sequence models

7 Convolutional neural networks

8 Attention and Transformer

9 Transfer learning with pretrained language models

PART 3 PUTTING INTO PRODUCTION

10 Best practices in developing NLP applications

11 Deploying and serving NLP applications

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