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Titlebook: Emotion Detection in Natural Language Processing; Federica Cavicchio Book 2025 The Editor(s) (if applicable) and The Author(s), under excl

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發(fā)表于 2025-3-21 19:36:01 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Emotion Detection in Natural Language Processing
編輯Federica Cavicchio
視頻videohttp://file.papertrans.cn/321/320575/320575.mp4
概述Discusses current challenges and limitations along with the ethical considerations on emotion annotation.Presents an accessible introduction to annotating emotions and explains how to improve NLP and
叢書名稱Synthesis Lectures on Human Language Technologies
圖書封面Titlebook: Emotion Detection in Natural Language Processing;  Federica Cavicchio Book 2025 The Editor(s) (if applicable) and The Author(s), under excl
描述.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..
出版日期Book 2025
關(guān)鍵詞Emotion Annotation; Deep Learning; Emotion Detection; Rule-based Approaches; Natural Language Understand
版次1
doihttps://doi.org/10.1007/978-3-031-72047-5
isbn_softcover978-3-031-72049-9
isbn_ebook978-3-031-72047-5Series ISSN 1947-4040 Series E-ISSN 1947-4059
issn_series 1947-4040
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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發(fā)表于 2025-3-21 20:57:56 | 只看該作者
Machine Learning Approaches to Emotion Detection,fined categories. For supervised learning, we focus on training models on labelled datasets to accurately recognize specific emotions. Finally, we address the challenges and limitations of both unsupervised and supervised methods for emotion detection, considering the trade-offs between the flexibil
板凳
發(fā)表于 2025-3-22 03:09:42 | 只看該作者
Deep Learning and Transformers for Emotion Detection,ifying emotion categories and dimensions. We conclude the Chapter by exploring the significance of transfer and generative learning in emotion detection, highlighting their solutions to data scarcity and ability to interpret?natural?language.
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發(fā)表于 2025-3-22 08:32:03 | 只看該作者
Challenges and Ethical Considerations of Emotion Detection, need for ethically responsible AI development. The Chapter concludes by advocating for integrating ethical principles throughout the AI development lifecycle to promote fairness, accountability, and transparency in AI.
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Molecular Mechanisms of Dementiafined categories. For supervised learning, we focus on training models on labelled datasets to accurately recognize specific emotions. Finally, we address the challenges and limitations of both unsupervised and supervised methods for emotion detection, considering the trade-offs between the flexibil
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發(fā)表于 2025-3-22 20:28:16 | 只看該作者
Molecular Biology Intelligence Unitifying emotion categories and dimensions. We conclude the Chapter by exploring the significance of transfer and generative learning in emotion detection, highlighting their solutions to data scarcity and ability to interpret?natural?language.
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1947-4040 archers and practitioners in the field of NLP and NLU and anyone interested in the intersection of natural language and emotion..978-3-031-72049-9978-3-031-72047-5Series ISSN 1947-4040 Series E-ISSN 1947-4059
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