Textual Emotion Classification Using Deep Broad Learning
In this book, the authors systematically and comprehensively discuss textual emotion classification by using deep broad learning. Since broad learning possesses certain advantages such as simple network structure, short training time and strong generalization ability, it is a new and promising frame...
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| Autors principals: | , |
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| Autor corporatiu: | |
| Format: | Electrònic eBook |
| Idioma: | anglès |
| Publicat: |
Cham :
Springer Nature Switzerland : Imprint: Springer,
2024.
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| Edició: | 1st ed. 2024. |
| Col·lecció: | Socio-Affective Computing,
11 |
| Matèries: | |
| Accés en línia: | https://doi.org/10.1007/978-3-031-67718-2 |
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Taula de continguts:
- Preface
- Acknowledgements
- Chapter 1. Introduction
- Chapter 2. BERT and Broad Learning for Textual Emotion Classification
- Chapter 3. Cascading Broad Learning for Textual Emotion Classification
- Chapter 4. Dual Broad Learning for Textual Emotion Classification
- Chapter 5. Single-source Domain Adaptation for Emotion Classification Using CNN-Based Broad Learning
- Chapter 6. Multi-source Domain Adaptation for Emotion Classification Using Bi-LSTM-Based Broad Learning. Chapter 7. Emotion Classification in Textual Conversations Using Deep Broad Learning
- Chapter 8. Rational Graph Attention Network and Broad Learning for Emotion Classification in Textual Conversations
- Chapter 9. Summary and Outlook.