the Academic NEST

Natural Language Processing (NLP) is a sub-field of Artificial Intelligence, linguistics, and computer science and is concerned with the generation, recognition, and understanding of human languages, both written and spoken. NLP systems examine the grammatical structure of sentences as well as the specific meanings of words, and then they utilize algorithms to extract meaning and produce results. Machine Learning and Deep Learning in Natural Language Processing aims at providing a review of current Neural Network techniques in the NLP field, in particular about Conversational Agents (chatbots), Text-to-Speech, management of non-literal content – like emotions, but also satirical expressions – and applications in the healthcare field.

NLP has the potential to be a disruptive technology in various healthcare fields, but so far little attention has been devoted to that goal. This book aims at providing some examples of NLP techniques that can, for example, restore speech, detect Parkinson’s disease, or help psychotherapists. This book is intended for a wide audience. Beginners will find useful chapters providing a general introduction to NLP techniques, while experienced professionals will appreciate the chapters about advanced management of emotion, empathy, and non-literal content.

This textbook systematically introduces the theories, methods, and algorithms for geotechnical reliability analysis. There are a lot of illustrative examples in the textbook such that readers can easily grasp the concepts and theories related to geotechnical reliability analysis. A unique feature of the textbook is that computer codes are also provided through carefully designed examples such that the methods and the algorithms described in the textbook can be easily understood. In addition, the computer codes are flexible and can be conveniently extended to analyze different types of realistic problems with little additional efforts.