Practical Natural Language Processing: A Comprehensive Guide to Building Real-World NLP Systems

Practical Natural Language Processing: A Comprehensive Guide to Building Real-World NLP Systems

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 NLPImplement and evaluate different NLP applications using machine learning and deep learning methodsFine-tune your NLP solution based on your business problem and industry verticalEvaluate various algorithms and approaches for NLP product tasks, datasets, and stagesProduce software solutions following best practices around release, deployment, and DevOps for NLP systemsUnderstand best practices, opportunities, and the roadmap for NLP from a business and product leader?s perspective

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Description

Editorial Reviews

Review

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

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

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.

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 themfor particular industry verticals, this is your guide. Softwareengineers and data scientists will learn how to navigate the maze ofoptions available at each step of the journey.

About the Author

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 authors’ collective wisdom is distilled in the book. The book has been reviewed and advised by researchers and scientists.

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From the Publisher

Practical NLP was born.

The authors have built and scaled solutions for over a decade at leading universities and technology companies. While mentoring colleagues and other engineers, they noticed a gap between the skill set of new engineers and those who have been in the industry for a while. Business and engineering leaders also suffer from these gaps and the authors began to understand them better with their NLP workshops.

Most of the online courses and books tackle NLP problems with toy use cases and popular datasets. While this teaches the readers general methods, it doesn’t give enough foundation to tackle new problems and develop complete solutions in the real world. Problems encountered while building real world applications such as data collection, working with noisy data and signals, incremental development of solutions, and issues involved in deployment as a part of a larger application are not dealt with by existing resources on the topic. The book was needed to bridge the gap between best practices and scenarios.

What is in the book?

The complete lifecycle of a typical NLP project is covered in this book. Some of the steps are very specific to NLP. Task-specific case studies and domain-specific guides are included in the book. It covers a wide range of tasks from text classification to question answering. It provides recipes to apply these tasks in different areas. The book covers case studies and best practices from the viewpoint of business, engineering and product leaders to help them run NLP projects smoothly.

The book does not go step by step explaining the code and all the concepts, due to the depth and breadth of the topics and scenarios that are covered. The details of the implementation can be found in the 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 a wide range of concepts, the book provides more than 450 references. The book will be a day-to-day cookbook that will give 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.

Some sections of the book are rudimentary and do not cover in-depth theoretical and technical details related to NLP concepts. Readers are expected to follow the documentations for various frameworks used in the code examples.

natural language programming

The book is for something.

  • A software engineer or a data scientist needs to build systems.
  • A machine learning engineer has to scale their systems.
  • A product manager needs to understand howNLP can be applied to their domain.
  • A business leader wants to start a new venture or incorporate the cutting edge of NLP in existing products.

Natural Language Processing

Natural Language Processing

Additional information

Best Sellers Rank

#522,995 in Kindle Store (See Top 100 in Kindle Store) #51 in Natural Language Processing (Kindle Store) #126 in Natural Language Processing (Books) #143 in Data Mining (Kindle Store)

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