Natural Language Processing with Spark Nlp: Learning to Understand Text at Scale
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Want to build an application that uses natural language text, but aren't sure where to start or what tools to use? This practical book gets you started with natural language processing from the basics to powerful modern techniques. Data scientists will learn how to build enterprise-quality NLP applications using deep learning and the Apache Spark distributed processing framework.
This guide includes concrete examples, practical and theoretical explanations, and hands-on exercises for NLP on Spark. You'll understand why these techniques work from machine learning, linguistic, and practical points of view.
This book shows you how to:
- Process text in a distributed environment using Spark-NLP, a production-ready library for NLP built on Spark
- Create, tune, and deploy your own word embeddings
- Adapt your NLP applications to multiple languages
- Use text in machine learning and deep learning
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 frameworks including UIMA and OpenNLP.