Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta, Harshit Surana
Endorsed by Zachary Lipton, Sebastian Ruder & Marc Najork
Foreword by Julian McAuleyRead the testimonials
Practical NLP focuses squarely on an overlooked demographic: the practitioners and business leaders in industry! While many great books focus on ML’s algorithmic fundamentals, this book exposes the anatomy of real-world systems: from e-commerce applications to virtual assistants. Painting a realistic picture of modern production systems, the book teaches not only deep learning, but also the heuristics and patchwork pipelines that define the (actual) state of the art for deployed NLP systems. The authors zoom out, teaching problem formulation, and aren’t afraid to zoom in on the grimy details, including handling messy data and sustaining live systems. This book will prove invaluable to industry professionals keen to build and deploy NLP in the wild.
This book does a great job bridging the gap between natural language processing (NLP) research and practical applications. From healthcare to e-commerce and finance, it covers many of the most sought-after domains where NLP is being put to use and walks through core tasks in a clear and understandable manner. Overall, the book is a great manual on how to get the most out of current NLP in your industry.
There are two kinds of computer science books on the market: academic textbooks that give you a deep understanding of a domain but can be difficult to access for a non-academic, and "cookbooks" that outline solutions to very specific problems without providing the technical foundations that would allow the reader to generalize the recipes. This book offers the best of both worlds: it is thorough yet accessible. It provides the reader with a solid foundation in natural-language processing,if you would like to go from zero to one in NLP, this book is for you!
There are text books or research papers or books on programming tips, but not a book that tells us how to build an end-to-end NLP system from scratch. I am happy to see this book on “Practical NLP”, which fills this much needed gap. The authors have meticulously, thoughtfully and lucidly covered each and every aspect of NLP that one has to be aware of while building large scale practical systems; at the same time, this book has also managed to cover a large number of examples and varied application areas and verticals. This book is a must for all aspiring NLP engineers, entrepreneurs who want to build companies around language technologies, and also academic researchers who would like to see their inventions reach the real users.
This book bridges the gap between theory and practice by explaining the underlying concepts while keeping in mind varied real-world deployments across different business verticals. There is much hard-fought practical advice from the trenches whether it is about tweaking parameters of open source libraries, setting up data pipelines for building models, or optimizing for fast inference. A must-read for engineers building NLP applications.
I feel this is not only an essential book for NLP practitioners, it is also a valuable reference for the research community to understand the problem spaces in real-world applications. I very much appreciate this book and wish this could be a long-term project with up-to-date NLP applications trending!
This book shows how to put NLP to practice. It bridges the ever widening gap between NLP theory and practical engineering. From fundamentals to hands on code examples of state of the art modeling techniques, this book provides a thoughtful and in-depth experience to the reader. The authors achieved a rare feat by simplifying the esoteric art of design and architecture of production quality machine learning systems. I wish I had access to this book early on in my professional career and evaded the mistakes I made along the way. I am deeply convinced that this book is an essential read for anybody involved in developing a robust, high-performing NLP system.
Practical NLP focuses on how to properly build NLP systems for the real world. Before this book, anyone taking an NLP model past the prototype stage for the first time had to rely on trial and error and a patchwork of blog posts from around the Internet. This book gives a consolidated look at modern practice, starting from an MVP and building up to examples for sophisticated use cases.
The field of Natural Language Processing has undergone a dramatic shift in recent years, both in terms of methodology and in terms of the applications supported. Methodological advances have ranged from new ways of representing documents to new techniques for language synthesis. With these have come new applications ranging from open-ended conversational systems to techniques that use natural language for model interpretability. Finally, these advances have seen natural language processing gain a foothold in related areas, such as computer vision and recommender systems, some of which my lab is working on with support from Amazon, Samsung and the National Science Foundation.
As NLP is expanding into these exciting new areas, so too has the audience of practitioners wanting to make use of NLP techniques. In the Data Science course (CSE 258) I take at UCSD which is often the most attended in the computer science department, I see more and more students are doing their projects on NLP based topics. Rapidly, NLP is becoming a necessary skill required by engineers and product managers, to scientists, students, and enthusiasts wishing to build applications on top of natural language data. On one hand, new tools and libraries for NLP and machine learning have made natural language modeling more accessible than ever. But on the other hand, resources for learning NLP must target this ever-growing and diverse audience. This is especially true for organizations that have recently adopted NLP, or for students working with natural language data for the first time.
It has been my pleasure over the last few years to collaborate with Bodhisattwa Majumder on exciting new applications in natural language processing and dialog, so I was thrilled to hear about his efforts, along with Sowmya Vajjala, Anuj Gupta, Harshit Surana, to write a book on NLP. They have a wide experience in scaling NLP including at early-stage startups, the MIT Media Lab, Microsoft Research, and Google AI.
I am excited by the end-to-end approach taken in their book, which will make it useful for a range of scenarios, and will help readers to work with the labyrinth of possible options while building NLP applications. I am especially thrilled by the focus on modern NLP applications such as chatbots, as well as the focus on interdisciplinary topics such as e-commerce and retail. These topics will be especially useful for industry leaders and researchers, and critically are topics that have been given limited coverage in existing textbooks. This book will be ideal both as a first resource to discover the field of natural language processing, as well as for seasoned practitioners looking to discover the latest developments in this exciting area.