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A multi-message passing framework based on heterogeneous graphs in conversational emotion recognition

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成果类型:
期刊论文
作者:
Meng, Tao;Shou, Yuntao;Ai, Wei;Du, Jiayi;Liu, Haiyan;...
通讯作者:
Ai, W
作者机构:
[Shou, Yuntao; Ai, Wei; Ai, W; Meng, Tao; Du, Jiayi] Cent South Univ Forestry & Technol, Coll Comp & Informat Engn, Changsha, Hunan, Peoples R China.
[Liu, Haiyan] Changsha Med Univ, Coll Informat Engn, Changsha, Hunan, Peoples R China.
[Li, Keqin] SUNY Coll New Paltz, Dept Comp Sci, New Paltz, NY 12561 USA.
通讯机构:
[Ai, W ] C
Cent South Univ Forestry & Technol, Coll Comp & Informat Engn, Changsha, Hunan, Peoples R China.
语种:
英文
关键词:
Emotion recognition in conversation;Heterogeneous Graph Neural Network;Multi-messaging;Self attention mechanism
期刊:
Neurocomputing
ISSN:
0925-2312
年:
2024
卷:
569
页码:
127109
基金类别:
CRediT authorship contribution statement Tao Meng: Investigation, Conception and design of study, Acquisition of data, Software, Writing the original manuscript. Yuntao Shou: Methodology, Analysis and interpretation of results, Writing – review & editing. Wei Ai: acquisition, Resources, Reviewing and editing. Jiayi Du: Validation, Supervision. Haiyan Liu: Software. Keqin Li: Reviewing and editing.
机构署名:
本校为第一且通讯机构
院系归属:
计算机与信息工程学院
摘要:
As an important development direction of natural language processing, emotion recognition in conversation (ERC) remains a challenge in sentiment analysis. Given the large-scale dialogue datasets and their wide application in the fields of recommendation systems and human–machine dialogue systems, researchers have begun to pay more attention to the issue of ERC. In recent research, the task of ERC has been largely based on the graph structure to model the speaker level. However, most existing studies simply splice multimodal features, and the h...

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