Table of Contents
Natural Language Processing with Spark NLP - Learning to Understand Text at Scale by Alex Thomas
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Book Summary
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
About the Author
Alex Thomas is a data scientist at Indeed. He has used natural language processing (NLP) and machine learning with clinical data, identity data, and now employer and jobseeker data. He has worked with Apache Spark since version 0.9, and has worked with NLP libraries and NLP frameworks including UIMA and OpenNLP.
Product Details
- Publication date: June 25, 2020
- Paperback: 364 pages
- Time to Complete: 9h
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Natural Language Processing (NLP): What Is Language, Text classification, Language modeling,
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Artificial Intelligence (AI): AI Fundamentals, AI Inventor: Arthur Samuel of IBM 1959 coined term Machine Learning. Synonym Self-Teaching Computers from 1950s. Experimental AI “Learning Machine” called Cybertron in early 1960s by Raytheon Company; ChatGPT, NLP, GAN, AI winter, The Singularity, AI FUD, Quantum FUD (Fake Quantum Computers), AI Propaganda, Quantum Propaganda, Cloud AI (AWS AI, Azure AI, Google AI-GCP AI-Google Cloud AI, IBM AI, Apple AI), Deep Learning (DL), Machine learning (ML), AI History, AI Bibliography, Manning AI-ML-DL-NLP-GAN Series, AI Glossary, AI Topics, AI Courses, AI Libraries, AI frameworks, AI GitHub, AI Awesome List. (navbar_ai - See also navbar_dl, navbar_ml, navbar_nlp, navbar_chatgpt)
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