中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 自动化研究所
  3. 中国科学院自动化研究所
  4. 复杂系统认知与决策实验室
Multi-source domain adaptation method for textual emotion classification using deep and broad learning

文献类型：期刊论文

作者Peng, Sancheng6; Zeng, Rong5; Cao, Lihong6; Yang, Aimin4; Niu, Jianwei3; Zong, Chengqing2; Zhou, Guodong1
刊名KNOWLEDGE-BASED SYSTEMS
出版日期2023-01-25
卷号260页码:9
ISSN号0950-7051
关键词Multi-domain Emotion classification BERT Broad learning Bi-LSTM
DOI10.1016/j.knosys.2022.110173
通讯作者Cao, Lihong(201610130@oamail.gdufs.edu.cn)
英文摘要Existing domain adaptation methods for classifying textual emotions have the propensity to focus on single-source domain exploration rather than multi-source domain adaptation. The efficacy of emotion classification is hampered by the restricted information and volume from a single source domain. Thus, to improve the performance of domain adaptation, we present a novel multi-source domain adaptation approach for emotion classification, by combining broad learning and deep learning in this article. Specifically, we first design a model to extract domain-invariant features from each source domain to the same target domain by using BERT and Bi-LSTM, which can better capture contextual features. Then we adopt broad learning to train multiple classifiers based on the domain-invariant features, which can more effectively conduct multi-label classification tasks. In addition, we design a co-training model to boost these classifiers. Finally, we carry out several experiments on four datasets by comparison with the baseline methods. The experimental results show that our proposed approach can significantly outperform the baseline methods for textual emotion classification.(c) 2022 Published by Elsevier B.V.
资助项目National Natural Sci- ence Foundation of China ; Ministry of Education of Humanities and Social Science project ; [61876205] ; [20YJAZH118]
WOS研究方向Computer Science
语种英语
出版者ELSEVIER
WOS记录号WOS:000905783200001
资助机构National Natural Sci- ence Foundation of China ; Ministry of Education of Humanities and Social Science project
源URL[http://ir.ia.ac.cn/handle/173211/51108]  
专题复杂系统认知与决策实验室
通讯作者Cao, Lihong
作者单位1.Soochow Univ, Sch Comp Sci & Technol, Suzhou, Peoples R China
2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China
3.Beihang Univ, State Key Lab Virtual Real Technol & Syst, Beijing 100191, Peoples R China
4.Lingnan Normal Univ, Sch Comp Sci & Intelligence Educ, Zhanjiang 524048, Peoples R China
5.South China Normal Univ, Guangdong Prov Key Lab Nanophoton Funct Mat & Devi, Guangzhou 510006, Peoples R China
6.Guangdong Univ Foreign Studies, Lab Language Engn & Comp, Guangzhou 510006, Peoples R China
推荐引用方式
GB/T 7714		 Peng, Sancheng,Zeng, Rong,Cao, Lihong,et al. Multi-source domain adaptation method for textual emotion classification using deep and broad learning[J]. KNOWLEDGE-BASED SYSTEMS,2023,260:9.
APA Peng, Sancheng.,Zeng, Rong.,Cao, Lihong.,Yang, Aimin.,Niu, Jianwei.,...&Zhou, Guodong.(2023).Multi-source domain adaptation method for textual emotion classification using deep and broad learning.KNOWLEDGE-BASED SYSTEMS,260,9.
MLA Peng, Sancheng,et al."Multi-source domain adaptation method for textual emotion classification using deep and broad learning".KNOWLEDGE-BASED SYSTEMS 260(2023):9.

入库方式： OAI收割

来源：自动化研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。