From the Inside Flap
THE PHILOSOPHY
We want to provide a holistic, yet, practical perspective which enables the reader to successfully build real world NLP solutions embedded in larger product setups. Thus, most chapters are accompanied by code walkthroughs in the associated git repository. The book is also supplemented with extensive references at the end of each chapter for the readers who want to delve deeper. Throughout the book, we start with a simple solution and incrementally build more complex solutions, by taking a Minimum Viable Product (MVP) approach, as commonly found in industry practice. We also give tips wherever possible based on our experience and learnings. Where possible, each chapter is accompanied by a discussion on the state of the art in that topic. Most chapters conclude with a case study taking real world use cases.
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 NLP 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.
SCOPE
This book gives a comprehensive view on building real world NLP applications. We will cover the complete lifecycle of a typical NLP project - right from data collection to deploying and monitoring the model. Some of these steps are applicable to any ML pipeline while some are very specific to NLP. We also introduce task-specific case studies and domain-specific guides to build an NLP system from scratch. Specifically we cover a gamut tasks ranging from text classification to question answering, information extraction to dialog systems. Similarly, we provide recipes to apply these tasks in domains ranging from e-commerce to healthcare, social media to finance. Owing to the depth and breadth of the topics and scenarios we cover, we will not go step by step explaining the code and all the concepts. For details of the implementation, we have provided detailed source code notebooks. The Code snippets given in the book cover the core logic and often skip introductory steps like setting up a library or importing a package as they are covered in the associated notebooks. To cover the wide range of concepts we have given more than 450 extensive references to delve deeper into these topics. This book will be a day-to-day cookbook giving you a pragmatic view while building any NLP system as well as be a stepping stone to broaden the application of NLP into your domain. From the Back Cover
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.
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. 
