北京邮电大学学报

  • EI核心期刊

北京邮电大学学报 ›› 2024, Vol. 47 ›› Issue (2): 18-23.

• 论文 • 上一篇    下一篇

基于语境与语义模态的多任务情感原因对抽取

刘宇鹏,冯贤杰,姚登举   

  1. 哈尔滨理工大学
  • 收稿日期:2023-02-11 修回日期:2023-05-04 出版日期:2024-04-28 发布日期:2024-01-24
  • 通讯作者: 刘宇鹏 E-mail:flyeagle99@126.com
  • 基金资助:
    国家自然科学基金;国家自然科学基金

Multi-task emotion cause pair extraction based on context and semantic modal

  • Received:2023-02-11 Revised:2023-05-04 Online:2024-04-28 Published:2024-01-24

摘要: 为了综合考虑更多模态信息,对语境和语义特征进行了建模,并将它们融合在一起以提取情感原因对。针对语境模态,采用了子句嵌入方法来获取情绪和原因的表示,并通过双因素注意力机制得到全局语境矩阵。同时,通过构建子句间语义的图神经网络,得到了局部语义特征。最后,通过主模态和辅助模态的匹配,得到了融合特征,以进行多任务预测,包括情感句、原因句和情感-原因对的抽取。实验结果表明,在抽取经典中文情感原因对数据时,相较于最佳基线系统,所提模型的F 测度提高了 2.2% 。

关键词: 情感原因对, 全局语境, 局部语义, 模态匹配

Abstract: In this paper, contextual and semantic features are modeled in detail, and Emotion Cause Pair Extraction (ECPC) is carried out on the fusion of two modal features. For context modal, BiLSTM is used to convert word embedding into clause embedding to get emotion and cause representation, and the global context matrix is obtained by two-factor attention mechanism. For semantic modal, local semantic features are obtained by constructing Graph Convolution networks (GCN) through inter-clause semantics. Finally, the fusion features are obtained by the main and auxiliary mode matching method for multi-task prediction, including emotional sentence, cause sentence and emotion-cause pair extraction task. Extensive experiments have been done to verify that contextual and semantic fusion system (CSF-ECPE) is significantly improved by 2.2% compared with the best baseline system in the classical Chinese ECPC data.

Key words: Emotion Cause Pair Extraction, Global Context, Local semantics, Modal Matching

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