Natural Language Processing with Spark NLP: Learning to Understand Text at Scale 1st Edition

Natural Language Processing with Spark NLP: Learning to Understand Text at Scale 1st Edition

If you’re looking to build an enterprise-grade application that processes natural language text and aren’t sure where to start, Natural Language Processing with Spark NLP by Alex Thomas is the comprehensive guide you need. Written for software engineers and data scientists, this book provides a practical, hands-on approach to building scalable NLP applications using deep learning and the Apache Spark NLP library.

Authored by Alex Thomas, principal data scientist at Wisecube, this book covers everything from the basics of NLP and deep learning to more advanced topics like sentiment analysis, tokenization, and named-entity recognition. Through real-world examples, theoretical explanations, and exercises, readers will gain the skills necessary to create efficient text-processing systems on the Spark framework.

The book is structured into four sections, guiding readers through the essentials of NLP, building applications, and deploying them:

Key Features:

• NLP Basics: Learn the fundamentals of natural language processing, NLP on Apache Spark, and deep learning principles.
• Building Blocks: Master techniques like tokenization, sentence segmentation, and named-entity recognition while understanding how they work and why they’re critical.
• Applications: Design, develop, and experiment with your own NLP applications using real-world examples.
• Building NLP Systems: Explore productionizing and deploying NLP models, including language support and system performance considerations.

Whether you’re a beginner or a seasoned professional, Natural Language Processing with Spark NLP offers the knowledge and tools needed to build highly scalable, robust NLP systems.

Table of Contents (Short Version)

1. Introduction to Natural Language Processing and Spark NLP
2. NLP Fundamentals on Apache Spark
3. Deep Learning Techniques for NLP
4. Tokenization and Sentence Segmentation
5. Named-Entity Recognition and Classification
6. Building Sentiment Analysis Systems
7. Creating Custom NLP Applications
8. Deploying and Scaling NLP Models
9. Human Language Considerations in NLP
10. Performance Optimization and System Maintenance

What is Natural Language Processing?

Natural Language Processing (NLP) is a branch of artificial intelligence (AI) that focuses on the interaction between computers and human language. It involves the development of algorithms and systems that can analyze, understand, and generate human language. Applications of NLP include speech recognition, text analysis, language translation, and sentiment analysis, making it essential for various industries such as healthcare, finance, and e-commerce.

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Product Details:

• Publisher: O’Reilly Media; 1st edition (June 25, 2020)
• ISBN-13: 9781492047766
• Language: English
• Print Length: 561 pages
• Format Type: PDF