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Practical Natural Language Processing - A Comprehensive Guide to Building Real-World NLP Systems by Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta

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Practical Natural Language Processing - A Comprehensive Guide to Building Real-World NLP Systems by Sowmya Vajjala

Practical Natural Language Processing - A Comprehensive Guide to Building Real-World NLP Systems by Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta

Natural Language Processing with Spark NLP - Learning to Understand Text at Scale by Alex Thomas

Practical Natural Language Processing

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Book Summary

Many books and courses tackle natural language processing (NLP) problems with toy use cases and well-defined datasets. But if you want to build, iterate, and scale NLP systems in a business setting and tailor them for particular industry verticals, this is your guide. Software engineers and data scientists will learn how to navigate the maze of options available at each step of the journey.

Through the course of the book, authors Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta, and Harshit Surana will guide you through the process of building real-world NLP solutions embedded in larger product setups. You’ll learn how to adapt your solutions for different industry verticals such as healthcare, social media, and retail.

With this book, you’ll:

Understand the wide spectrum of problem statements, tasks, and solution approaches within NLP
Implement and evaluate different NLP applications using machine learning and deep learning methods
Fine-tune your NLP solution based on your business problem and industry vertical
Evaluate various algorithms and approaches for NLP product tasks, datasets, and stages
Produce software solutions following best practices around release, deployment, and DevOps for NLP systems
Understand NLP best practices, opportunities, and the roadmap for NLP from a business and product leader’s perspective

Reviews

From an Amazon Customer: ”… the word “practical” in the title might make you think about the word “applied”, as in NLP code. That was what I was expecting. This is a great book to give a manager to read about NLP. They will understand things at a high level after reading it. However it just does not get the job done for someone like myself looking to apply this knowledge.“

“This book is ideal both as a first resource to discover the field of natural language processing and a guide for seasoned practitioners looking to discover the latest developments in this exciting area.” - Julian McAuley, Professor, UC San Diego

”Practical NLP focuses squarely on an overlooked demographic: the practitioners and business leaders in industry!“ - Zachary Lipton, Scientist at Amazon AI, Author of Dive into Deep Learning, Professor, Carnegie Mellon University

“This book does a great job bridging the gap between natural language processing research and practical applications.” - Sebastian Ruder Scientist, Google DeepMind, Author of newsletter NLP News

“This book offers the best of both worlds: textbooks and 'cookbooks'. If you would like to go from zero to one in NLP, this book is for you!” - - Marc Najork, Director, Google AI, ACM & IEEE Fellow

“This book is a must for all aspiring NLP engineers, entrepreneurs who want to build companies around language technologies.” - Monojit Choudhury, Principal Researcher, Microsoft, Faculty at IIT Kharagpur

“There is much hard-fought practical advice from the trenches. A must-read for engineers building NLP applications.” - Vinayak Hegde, CTO-in-Residence, Microsoft For Startups

“I feel this is not only an essential book for NLP practitioners, it is also a valuable reference for the NLP research community.” - Mengting Wan, Data Scientist at Airbnb, Microsoft Research Fellow

“The authors achieved a rare feat by simplifying the esoteric art of design and architecture of production quality ML systems.” - Siddharth Sharma, ML Engineer, Facebook

“This book gives a consolidated look at modern NLP practice, starting from an MVP and building up to examples for sophisticated NLP use cases.” - Ed Harris, CEO and co-founder at SharpestMinds (YC W18)

About the Author

Sowmya Vajjala has a PhD in Computational Linguistics from University of Tubingen, Germany. She currently works as a research officer at National Research Council, Canada’s largest federal research and development organization. Her past work experience spans both academia as a faculty at Iowa State University, USA as well as industry at Microsoft Research and The Globe and Mail.

Bodhisattwa Majumder is a doctoral candidate in NLP and ML at UC San Diego. Earlier he studied at IIT Kharagpur where he graduated summa cum laude. Previously, he built large-scale NLP systems at Google AI Research and Microsoft Research, which went into products serving millions of users. Currently, he is also leading his university team in the Amazon Alexa Prize for 2019-2020.

Anuj Gupta has built NLP and ML systems at Fortune 100 companies as well as startups as a senior leader. He has incubated and led multiple ML teams in his career. He studied computer science at IIT Delhi and IIIT Hyderabad. He is currently Head of Machine Learning and Data Science at Vahan Inc. Above all, he is a father and husband.

Harshit Surana is founder at DeepFlux Inc. He has built and scaled ML systems at several Silicon Valley startups as a founder and an advisor. He studied computer science at Carnegie Mellon University where he worked with the MIT Media Lab on common sense AI. His research in NLP has received over 200 citations.

The authors have been working on NLP problems since 2006. They hail from Carnegie Mellon, UC San Diego, U of Tübingen, and the Indian Institutes of Technology. They have built and deployed NLP and ML systems in both academia and industry, including Fortune 100 companies, Silicon Valley startups, the MIT Media Lab, Microsoft Research and Google AI. They have also taught NLP courses at US universities as a faculty and published dozens of research papers in the field with hundreds of citations. The book distills the authors' collective wisdom for building and iterating NLP systems. The book is also advised and reviewed by researchers and scientists from Microsoft, Facebook, Spotify and Stanford University.