Emotion Detection in Natural Language Processing (Synthesis Lectures on Human Language Technologies) - Tapa blanda

Acerca del autor

Federica Cavicchio, Ph.D., is a Researcher at the University of Salento. Additionally, she is a scientific consultant with extensive experience assisting companies with developing Artificial Intelligence, Natural Language Processing, and Natural Language Understanding applications. Dr. Cavicchio obtained her Ph.D. in Cognitive Neuroscience at CIMeC (Centre for Interdepartmental studies on Mind and Brain), University of Trento, focusing on computational aspects of the expression of emotion. She was awarded a Marie Curie fellowship, which allowed her to work on bilingualism at the University of Birmingham (UK). Subsequently, she was a post-doc at the University of Haifa, where she investigated the compositionality of emotional expressions.

De la contraportada

This book provides a practical guide on annotating emotions in natural language data and showcases how these annotations can improve Natural Language Processing (NLP) and Natural Language Understanding (NLU) models and applications. The author presents an introduction to emotion as well as the ethical considerations on emotion annotation. State-of-the-art approaches to emotion annotation in NLP and NLU including rule-based, machine learning, and deep learning applications are addressed. Theoretical foundations of emotion and the implication on emotion annotation are discussed along with the current challenges and limitations in emotion annotation. This book is appropriate for researchers and practitioners in the field of NLP and NLU and anyone interested in the intersection of natural language and emotion.

In addition, this book:

• Discusses current challenges and limitations along with the ethical considerations on emotion annotation
• Presents an accessible introduction to annotating emotions and discusses how to improve NLP and NLU applications
• Provides overview of the current state-of-the-art approaches to emotion annotation in NLP