Practical Natural Language Processing: A Pragmatic Approach to Processing and Analyzing Language Data

Anuj Sowmya|Bohisattwa Majumder|Anuj Gupta|Harshit Surana

ISBN 10: 1492054054 / ISBN 13: 9781492054054

Published by O'Reilly Media, 2020

About this Item

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 .

Seller Inventory # 329135896

Bibliographic Details

• Title: Practical Natural Language Processing: A Pragmatic Approach to Processing and Analyzing Language Data
• Publisher: O'Reilly Media
• Publication Date: 2020
• Language: English
• ISBN 10: 1492054054
• ISBN 13: 9781492054054
• Binding: Soft cover
• Condition: New
• Seller catalogs:
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About this title

Synopsis

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

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.