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. Natural Language Processing (NLP) is a sub-field of Computer Science, information engineering, and Artificial Intelligence (AI) that deals with the computational processing and comprehension of human languages. Machine Learning (ML) for NLP and text analytics involves a set of statistical techniques for identifying parts of speech, named entities, sentiments, emotions, and other aspects of text. ML is a subset of AI which deals with the study of algorithms and statistical methods that computer systems use to effectively perform a specific task. ML does this without using explicit instructions, relying on patterns and learns from the dataset to make predictions or decisions. ML algorithms are classified into supervised, semi-supervised, active learning, reinforcement, and unsupervised learning. 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. Natural Language Processing (NLP) is a subset of AI, linguistics and computer science, and is concerned with generation, recognition and understanding of human language, both written and spoken. NLP systems examine the grammatical structure of sentences as well as the specific meanings of words, and then 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 Natural Language Processing 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 the fields of healthcare, 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. The book is intended for a wide audience: beginners will find chapters providing a general introduction to the NLP techniques useful, while experienced professionals would appreciate the chapters about advanced management of emotion, empathy, and non-literal content. Cover 1 Half Title 2 Title Page 4 Copyright Page 5 Contents 6 Preface 8 Editors 14 Contributors 15 Part I: Introduction 18 Chapter 1: Introduction to Machine Learning, Deep Learning, and Natural Language Processing 20 Part II: Overview of Conversational Agents 32 Chapter 2: Conversational Agents and Chatbots: Current Trends 34 Chapter 3: Unsupervised Hierarchical Model for Deep Empathetic Conversational Agents 70 Part III: Sentiment and Emotions 92 Chapter 4: EMOTRON: An Expressive Text-to-Speech 94 Part IV: Fake News and Satire 112 Chapter 5: Distinguishing Satirical and Fake News 114 Chapter 6: Automated Techniques for Identifying Claims and Assisting Fact Checkers 142 Part V: Applications in Healthcare 164 Chapter 7: Whisper Restoration Combining Real- and Source-Model Filtered Speech for Clinical and Forensic Applications 166 Chapter 8: Analysis of Features for Machine Learning Approaches to Parkinson’s Disease Detection 186 Chapter 9: Conversational Agents, Natural Language Processing, and Machine Learning for Psychotherapy 201 INDEX 241 Conversational,Agent,(CA);,Chatbot;,emotion,irony;,Text-to-Speech,(TTS);,Irony;,Neural,Networks;,empathy;,fake,news;,Parkinson Conversational Agent (CA),Chatbot,emotion irony,Text-to-Speech (TTS),Irony,Neural Networks,empathy,fake news,Parkinson
