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.
Purchase of the print book includes a free eBook in PDF, Kindle, and
ePub formats from Manning Publications.
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
