Natural Language Processing with Spark NLP: Learning to Understand Text at Scale

Natural Language Processing with Spark NLP: Learning to Understand Text at Scale
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Book Description
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

Content

I. Basics
1. Getting Started
2. Natural Language Basics
3. NLP on Apache Spark
4. Deep Learning Basics

II. Building Blocks
5. Processing Words
6. Information Retrieval
7. Classification and Regression
8. Sequence Modeling with Keras
9. Information Extraction
10. Topic Modeling
11. Word Embeddings

III. Applications
12. Sentiment Analysis and Emotion Detection
13. Building Knowledge Bases
14. Search Engine
15. Chatbot
16. Object Character Recognition

IV. Building NLP Systems
17. Supporting Multiple Languages
18. Human Labeling
19. Productionizing NLP Applications

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