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Practical Natural Language Processing: A Comprehensive Guide to Building Real-World NLP Systems Paperback – 30 June 2020
Sowmya Vajjala (Author) Find all the books, read about the author, and more. See search results for this author |
Bodhisattwa Majumder (Author) Find all the books, read about the author, and more. See search results for this author |
Anuj Gupta (Author) Find all the books, read about the author, and more. See search results for this author |
Harshit Surana (Author) Find all the books, read about the author, and more. See search results for this author |
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If you want to build, iterate and scale NLP systems in a business setting and to tailor them for various industry verticals, this is your guide.
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 that uses rule based systems or traditional machine learning will be apt. As you accumulate more data, more sophisticated―and often data intensive―ML techniques can be used including deep learning. At each step of this journey, there are dozens of alternative approaches you can take. This book helps you navigate this maze of options.
- Print length375 pages
- LanguageEnglish
- PublisherO'Reilly Media, Inc, USA
- Publication date30 June 2020
- Dimensions17.78 x 2.34 x 23.34 cm
- ISBN-101492054054
- ISBN-13978-1492054054
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Product description
Review
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 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 practice, starting from an MVP and building up to examples for sophisticated use cases. - Ed Harris, CEO and co-founder at SharpestMinds (YC W18)
From the Author
- 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
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
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.
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Product details
- Publisher : O'Reilly Media, Inc, USA (30 June 2020)
- Language : English
- Paperback : 375 pages
- ISBN-10 : 1492054054
- ISBN-13 : 978-1492054054
- Dimensions : 17.78 x 2.34 x 23.34 cm
- Best Sellers Rank: 27,599 in Books (See Top 100 in Books)
- 11 in Natural Language Processing
- 21 in Data Mining
- 81 in Management Information Systems
- Customer Reviews:
About the authors
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.
Discover more of the author’s books, see similar authors, read author blogs, and more
Discover more of the author’s books, see similar authors, read author blogs, and more
Harshit Surana is a cofounder at DeepFlux. 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.
Customer reviews
Top reviews from other countries

Then I requested a replacement and same story happened again.
Much as I am focused on the content of the book the form in which it comes is also important.
You can't sell books that are loosing pages so easily.

How is it possible?
The book I got was "Infrastructure as Code", see Photo., but the cover says Practical Natural Language Processing.
Usually, I wouldn't give a bad review, but I read that many users complained about the book's quality. Well, I wouldn't suggest to buy the book until they fix their quality issues.


Reviewed in Germany on 6 April 2022
How is it possible?
The book I got was "Infrastructure as Code", see Photo., but the cover says Practical Natural Language Processing.
Usually, I wouldn't give a bad review, but I read that many users complained about the book's quality. Well, I wouldn't suggest to buy the book until they fix their quality issues.





Reviewed in Spain on 26 August 2020




Reviewed in France on 25 October 2020

