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Moka-ADA: adversarial domain adaptation with model-oriented knowledge adaptation for cross-domain sentiment analysis

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成果类型:
期刊论文
作者:
Zhang, Maoyuan;Li, Xiang;Wu, Fei
通讯作者:
Xiang Li
作者机构:
[Wu, Fei; Li, Xiang; Zhang, Maoyuan] Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smart, Wuhan 430079, Hubei, Peoples R China.
[Wu, Fei; Li, Xiang; Zhang, Maoyuan] Cent China Normal Univ, Sch Comp, Wuhan 430079, Hubei, Peoples R China.
[Wu, Fei; Li, Xiang; Zhang, Maoyuan] Cent China Normal Univ, Natl Language Resources Monitor & Res Ctr Network, Wuhan 430079, Hubei, Peoples R China.
通讯机构:
[Xiang Li] H
Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, China<&wdkj&>School of Computer, Central China Normal University, Wuhan, China<&wdkj&>National Language Resources Monitor and Research Center for Network Media, Central China Normal University, Wuhan, China
语种:
英文
关键词:
Cross-domain sentiment analysis;Domain adaptation;Adversarial learning;Knowledge distillation
期刊:
JOURNAL OF SUPERCOMPUTING
ISSN:
0920-8542
年:
2023
卷:
79
期:
12
页码:
13724-13743
基金类别:
This work is supported by the Fundamental Research Funds of the National Language Committee (Grant No. YB135-40).
机构署名:
本校为第一机构
院系归属:
计算机学院
摘要:
Cross-domain sentiment analysis (CDSA) aims to overcome domain discrepancy to judge the sentiment polarity of the target domain lacking labeled data. Recent research has focused on using domain adaptation approaches to address such domain migration problems. Among them, adversarial learning performs domain distribution alignment via domain confusion to transfer domain-invariant knowledge. However, this method that transforms feature representations to be domain-invariant tends to align only the marginal distribution, and may inevitably distort the original feature representations containing di...

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