Deep Learning is becoming increasingly important in a technology-dominated world. However, the building of computational models that accurately represent linguistic structures is complex, as it involves an in-depth knowledge of neural networks, and the understanding of advanced mathematical concepts such as calculus and statistics. This book makes these complexities accessible to those from a humanities and social sciences background, by providing a clear introduction to Deep Learning for Natural Language Processing (NLP). It covers both theoretical and practical aspects, and assumes minimal knowledge of Machine Learning, explaining the theory behind natural language in an easy-to-read way. It includes pseudo code for the simpler algorithms discussed, and actual Python code for the more complicated architectures, using modern Deep Learning libraries such as PyTorch and Hugging Face. Providing the necessary theoretical foundation and practical tools, this book will enable readers to immediately begin building real-world, practical natural language processing systems.
Existing Deep Learning and Natural Language Processing books generally fall into two camps. The first camp focuses on the theoretical foundations of Deep Learning. This is certainly useful to the aforementioned readers, as one should understand the theoretical aspects of a tool before using it. However, these books tend to assume the typical background of a Machine Learning researcher and, as a consequence, we have often seen students who do not have this background rapidly get lost in such material. To mitigate this issue, the second type of book that exists today focuses on the Machine Learning practitioner β that is, on how to use Deep Learning software, with minimal attention paid to the theoretical aspects. We argue that focusing on practical aspects is similarly necessary but not sufficient. Considering that deep learning frameworks and libraries have become fairly complex, the chance of misusing them due to theoretical misunderstandings is high. We have commonly seen this problem in our courses too.
This book therefore aims to bridge the theoretical and practical aspects of Deep Learning for Natural Language Processing. We cover the necessary theoretical background and assume minimal Machine Learning background from the reader. Our aim is that anyone who took introductory linear algebra and calculus courses will be able to follow the theoretical material. To address practical aspects, this book includes pseudocode for the simpler algorithms discussed and actual Python code for the more complicated architectures. The code should be understandable to anyone who has taken a Python programming course. After reading this book, we expect that the reader will have the necessary foundation to immediately begin building real-world, practical Natural Language Processing systems, and to expand their knowledge by reading research publications on these topics.