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_223
100 1 _aPeng, Sancheng.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aTextual Emotion Classification Using Deep Broad Learning
_h[electronic resource] /
_cby Sancheng Peng, Lihong Cao.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2024.
300 _aXV, 155 p. 46 illus., 45 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
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490 1 _aSocio-Affective Computing,
_x2509-5714 ;
_v11
505 0 _aPreface -- 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 _aIn 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.
541 _fUABC ;
_cPerpetuidad
650 0 _aNatural language processing (Computer science).
650 0 _aMachine learning.
650 0 _aComputational linguistics.
650 0 _aArtificial intelligence.
650 1 4 _aNatural Language Processing (NLP).
650 2 4 _aMachine Learning.
650 2 4 _aComputational Linguistics.
650 2 4 _aArtificial Intelligence.
700 1 _aCao, Lihong.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031677175
776 0 8 _iPrinted edition:
_z9783031677199
776 0 8 _iPrinted edition:
_z9783031677205
830 0 _aSocio-Affective Computing,
_x2509-5714 ;
_v11
856 4 0 _zLibro electrónico
_uhttp://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-3-031-67718-2
912 _aZDB-2-SBL
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