Abstract: 

Many books and courses tackle natural language processing (NLP) problems with toy use cases and well-defined datasets. This is anything but how NLP systems are built in industry.
If you want to build, iterate, and scale NLP systems in a business setting and tailor them for particular industry verticals, this book is your guide. Through the book, the authors 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. Not only practioners but also business leaders will learn how to navigate the maze of options available at each step of the journey.

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 best practices, opportunities, and the roadmap for NLP from a business and product leader’s perspective


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

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

Bio: 

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

Harshit Surana is a co-founder at DeepFlux Inc. He has built and scaled ML systems and engineering pipelines 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.

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 and 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.