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Название: Natural Language Processing with Spark NLP: Learning to Understand Text at Scale, 1st Edition Автор: Alex Thomas Издательство: O'Reilly Media Год: 2020 Страниц: 366 Язык: английский Формат: epub Размер: 10.1 MB
Natural language processing (NLP) is a field of study concerned with processing language data. We will be focusing on text, but natural language audio data is also a part of NLP. Dealing with natural language text data is difficult. The reason it is difficult is that it relies on three fields of study: linguistics, software engineering, and machine learning. It is hard to find the expertise in all three for most NLP-based projects. Fortunately, you don’t need to be a world-class expert in all three fields to make informed decisions about your application. As long as you know some basics, you can use libraries built by experts to accomplish your goals.
If you want to build an enterprise-quality application that uses natural language text but aren’t sure where to begin or what tools to use, this practical guide will help get you started. Alex Thomas, principal data scientist at Wisecube, shows software engineers and data scientists how to build scalable natural language processing (NLP) applications using deep learning and the Apache Spark NLP library.
Through concrete examples, practical and theoretical explanations, and hands-on exercises for using NLP on the Spark processing framework, this book teaches you everything from basic linguistics and writing systems to sentiment analysis and search engines. You’ll also explore special concerns for developing text-based applications, such as performance.
In four sections, you’ll learn NLP basics and building blocks before diving into application and system building:
Basics: Understand the fundamentals of natural language processing, NLP on Apache Stark, and deep learning Building blocks: Learn techniques for building NLP applications—including tokenization, sentence segmentation, and named entity recognition—and discover how and why they work Applications: Explore the design, development, and experimentation process for building your own NLP applications Building NLP systems: Consider options for productionizing and deploying NLP models, including which human languages to support
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