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...
Saved in:
| Main Authors: | , |
|---|---|
| 企业作者: | |
| 格式: | 电子 电子书 |
| 语言: | 英语 |
| 出版: |
Cham :
Springer Nature Switzerland : Imprint: Springer,
2024.
|
| 版: | 1st ed. 2024. |
| 丛编: | Socio-Affective Computing,
11 |
| 主题: | |
| 在线阅读: | https://doi.org/10.1007/978-3-031-67718-2 |
| 标签: |
添加标签
没有标签, 成为第一个标记此记录!
|
MARC
| LEADER | 00000nam a22000005i 4500 | ||
|---|---|---|---|
| 001 | 978-3-031-67718-2 | ||
| 003 | DE-He213 | ||
| 005 | 20260810115228.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 240928s2024 sz | o |||| 0|eng d | ||
| 020 | |a 9783031677182 |9 978-3-031-67718-2 | ||
| 024 | 7 | |a 10.1007/978-3-031-67718-2 |2 doi | |
| 050 | 4 | |a QA76.9.N38 | |
| 072 | 7 | |a UYQL |2 bicssc | |
| 072 | 7 | |a COM073000 |2 bisacsh | |
| 072 | 7 | |a UYQL |2 thema | |
| 082 | 0 | 4 | |a 006.35 |2 23 |
| 100 | 1 | |a Peng, Sancheng. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Textual Emotion Classification Using Deep Broad Learning |h [electronic resource] / |c by Sancheng Peng, Lihong Cao. |
| 250 | |a 1st ed. 2024. | ||
| 264 | 1 | |a Cham : |b Springer Nature Switzerland : |b Imprint: Springer, |c 2024. | |
| 300 | |a 1 online resource (XV, 155 p. 46 illus.). |b 45 illus. in color. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 341 | 0 | |b Table of contents navigation |2 onix | |
| 341 | 0 | |b Single logical reading order |2 onix | |
| 341 | 0 | |b Short alternative textual descriptions |2 onix | |
| 341 | 0 | |b Use of color is not sole means of conveying information |2 onix | |
| 341 | 0 | |b Use of high contrast between text and background color |2 onix | |
| 341 | 0 | |b Next / Previous structural navigation |2 onix | |
| 341 | 0 | |b All non-decorative content supports reading without sight |2 onix | |
| 347 | |a text file |b PDF |2 rda | ||
| 490 | 1 | |a Socio-Affective Computing, |x 2509-5714 ; |v 11 | |
| 505 | 0 | |a 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. | |
| 520 | |a 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 framework for textual emotion classification in artificial intelligence. As a result, how to combine deep and broad learning has become a new trend of textual emotion classification, a booming topic in both academia and industry. For a better understanding, both quantitative and qualitative results are present in figures, tables, or other suitable formats to give the readers the broad picture of this topic along with unique insights of common sense and technical details, and to pave a solid ground for their forthcoming research or industry applications. In a progressive manner, the readers will gain exclusive knowledge in textual emotion classification using deep broad learning and be inspired to further investigate this underexplored domain. With no other similar book existing in the literature, the authors aim to make the book self-contained for newcomers, only a few prerequisites being expected from the readers. The book is meant as a reference for senior undergraduates, postgraduates, scientists and researchers interested to have a quick idea of the foundations and research progress of security and privacy in federated learning, and it can equally well be used as a textbook by lecturers, tutors, and undergraduates. | ||
| 532 | 8 | |a Accessibility summary: This PDF does not fully comply with PDF/UA standards, but does feature limited screen reader support, described non-text content (images, graphs), bookmarks for easy navigation and searchable, selectable text. Users of assistive technologies may experience difficulty navigating or interpreting content in this document. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at accessibilitysupport@springernature.com. | |
| 532 | 8 | |a No reading system accessibility options actively disabled | |
| 532 | 8 | |a Publisher contact for further accessibility information: accessibilitysupport@springernature.com | |
| 588 | |a Print version record. | ||
| 650 | 0 | |a Natural language processing (Computer science). | |
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Computational linguistics. | |
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 1 | 4 | |a Natural Language Processing (NLP). |
| 650 | 2 | 4 | |a Machine Learning. |
| 650 | 2 | 4 | |a Computational Linguistics. |
| 650 | 2 | 4 | |a Artificial Intelligence. |
| 700 | 1 | |a Cao, Lihong. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9783031677175 |
| 776 | 0 | 8 | |i Printed edition: |z 9783031677199 |
| 776 | 0 | 8 | |i Printed edition: |z 9783031677205 |
| 830 | 0 | |a Socio-Affective Computing, |x 2509-5714 ; |v 11 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/978-3-031-67718-2 |
| 912 | |a ZDB-2-SBL | ||
| 912 | |a ZDB-2-SXB | ||
| 950 | |a Biomedical and Life Sciences (SpringerNature-11642) | ||
| 950 | |a Biomedical and Life Sciences (R0) (SpringerNature-43708) | ||