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基于双图注意力网络的篇章级散文情绪变化分析方法

A Document-level Emotion Change Analysis Method Based on DualGATs for Prose

  • 摘要: 在散文中,作者的情绪会伴随着文章的段落或者句子发生变化,例如,从悲伤到快乐、从喜悦到愤怒。为分析情绪变化,该文构建散文情绪变化数据集,提出一种基于双图注意力网络的多种知识融合的情绪变化分析方法。首先,引入意象知识库,建立融合意象知识的句子表示;其次,构建上下文带权依赖图和语篇带权依赖图,通过融合上下文知识和语篇结构,建立融合上下文知识和语篇结构的句子表示;同时设计愉悦效价识别层,获得融合愉悦效价信息的句子表示;最后,将以上三种表示进行拼接,通过全连接网络得到最终的情绪变化结果。实验结果表明,该文提出的方法可以有效地识别情绪变化,并为散文阅读理解中的思想情绪变化类问题的解答提供帮助。

     

    Abstract: In Prose, the author's emotions evolve within passages or sentences, transitioning between sadness, happiness, joy and anger. This paper constructs a dataset of prose emotion changes and proposes an emotion analysis method based on Dual-Graph Attention Network. Firstly, this paper incorporates image knowledge to get the deep representation of sentence. Secondly, the context-weighted dependency graph and the discourse-weighted dependency graph are constructed, and the sentence representation is established by integrating context knowledge and discourse structure. Additionally, a pleasure valence recognition layer is designed to capture the integrate pleasure valence information. Experimental results indicate the method is effective for identifying emotion changes.

     

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