Practical Natural Language Processing

Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta, Harshit Surana


Endorsed by Zachary Lipton, Sebastian Ruder & Marc Najork

Foreword by Julian McAuley

Read the testimonials

Practical NLP Is All You Need

This book is your guide to build, iterate and scale NLP systems and to tailor them for various industry verticals

Increase your depth

Consider the task of building a chatbot or text classification system at your organization. In the beginning there may be little or no data to work with. At this point a basic solution using rule based systems or traditional machine learning will be apt. As you accumulate more data, more sophisticated ML techniques (which are often data intensive) can be used including deep learning. At each step of this journey there are dozens of alternative approaches one can take. This book will help you navigate this maze of options.

Expand your breadth

You will also learn to adapt your solutions for different industry verticals like healthcare, social media and retail. Moreover, you will learn about specific caveats you will encounter in each. You will learn how to analyze health records, detect fake news, extract attributes from e-commerce products and much more.

What the experts say

Zachary Lipton

Scientist at Amazon AI

Author of Dive into Deep Learning


Practical NLP focuses squarely on an overlooked demographic: the practitioners and business leaders in industry!

Sebastian Ruder

Scientist, Google DeepMind

Author of newsletter NLP News


This book does a great job bridging the gap between natural language processing research and practical applications.

Marc Najork

Director, Google AI

ACM & IEEE Fellow


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!

Monojit Choudhury

Principal Researcher, Microsoft

Faculty at IIT Kharagpur


This book is a must for all aspiring NLP engineers, entrepreneurs who want to build companies around language technologies.


Vinayak Hegde

CTO-in-Residence

Microsoft For Startups


There is much hard-fought practical advice from the trenches. A must-read for engineers building NLP applications.

Mengting Wan

Data Scientist (ML & NLP) at Airbnb

Microsoft Research Fellow


I feel this is not only an essential book for NLP practitioners, it is also a valuable reference for the research community.

Siddharth Sharma

ML Engineer

Facebook


The authors achieved a rare feat by simplifying the esoteric art of design and architecture of production quality ML systems.

Ed Harris

CEO and co-founder at

SharpestMinds (YC W18)


This book gives a consolidated look at modern practice, starting from an MVP and building up to examples for sophisticated use cases.


Julian McAuley

Professor at UC San Diego


I am excited by the end-to-end approach taken in this book, which make it useful for a range of scenarios and help readers to work with the labyrinth of possible options while building NLP applications. I was thrilled to hear about efforts by Bodhisattwa, Sowmya, Anuj and Harshit to write a book on NLP. They have a wide experience in scaling NLP including at early-stage startups, the MIT Media Lab, Microsoft Research, and Google AI. 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.

Read full foreword & testimonials

We wrote the book for

  • a software engineer or a data scientist who needs to build real-world NLP systems
  • a machine learning engineer who has to iterate and scale NLP systems
  • a product manager who needs to understand NLP and how it can be applied to their domain
  • a business leader who wants to start a new venture based on NLP or incorporate the cutting edge of NLP in existing products

Please note that readers pursuing cutting-edge research in NLP may find some sections of the book rudimentary as we do not cover in-depth theoretical and technical details related to NLP concepts. Moreover, we expect the readers to follow the respective documentations for various frameworks we use in our code examples.

Know what you will learn

About the authors

Sowmya Vajjala


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


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


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


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.

What you will learn?

Through the course of the book, we will guide you through the process of building real-world NLP solutions embedded in larger product setups via a compendium of over 450 references. 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.

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