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

Many books and courses cover natural language processing (NLP) problems involving toy use cases and well-defined data sets. However, if you want to build, clone, and extend NLP systems in your business environment, and tailor them for specific industries, this is your guide. Software engineers and data scientists  learn  to navigate the maze of options available at every step.

Throughout the book, authors Soumya Wajala, Bodhisattva Majumder, Anuj Gupta, and Harshit Surana  guide you through the process of creating a real-world NLP solution embedded in a large-scale product installation. You will learn how to tailor solutions for different industries such as healthcare, social media, and retail.

This book covers

This book provides a comprehensive guide to creating real-world NLP applications. It covers the entire lifecycle of a typical NLP project, from data collection to model deployment and monitoring. Some of these steps are applicable to any machine learning pipeline, and some are  specific to NLP. The book also provides task-specific case studies and topical guides for building an NLP system from scratch. Specifically, it covers  tasks ranging from classifying text  to answering questions, extracting information, and ending up with dialog systems. Likewise, we provide recipes for applying these tasks in fields as diverse as e-commerce to healthcare, and social media to law. The book also covers case studies and best practices from the perspectives of business leaders, engineers, and product developers to get your NLP projects running smoothly.

Due to the depth and breadth of the topics and scenarios  covered, this book does not provide step-by-step explanations of the code and all  concepts. See the detailed source code notebook for implementation details. The code snippets  in the book cover the basic logic and often skip introductory steps like setting up a library or importing a package, as described in each notebook. To cover a wide range of concepts, the book contains more than 450 extensive links to more detailed information on these topics. This book will become a daily cookbook, providing a practical perspective on building any NLP system, as well as  a stepping stone to expanding the use of NLP in the field. Readers of cutting-edge research in

NLP may find some sections of the book rudimentary because they do not cover the in-depth theoretical and technical details associated with NLP concepts. Readers should also follow the relevant documentation for the various frameworks used in the code examples.

This book is for

• Software engineers or  data scientists who need to create real-world NLP systems.
• Machine learning engineers who need to clone and extend NLP systems.
• Product managers who need to understand NLP and how it can be applied in their field.
• Business leaders looking to launch new NLP-based ventures or integrate advanced NLP technologies into existing products.

This book helps you to

• Understand the broad problem descriptions, tasks, and  approaches to solving in the framework of NLP.
• Implement and evaluate a variety of NLP applications using machine learning and deep learning techniques.
• Customise NLP solutions based on  business challenges and industries
  Evaluate different algorithms and approaches for tasks, data sets, and NLP steps.
• Build software solutions by following  release, deployment, and DevOps best practices for NLP systems.
• Understand best practices, opportunities, and roadmaps for NLP from the perspective of business and product leaders.