Residual-Duet Network with Tree Dependency Representation for Chinese Question-Answering Sentiment Analysis

Guangyi Hu, Chongyang Shi, Shufeng Hao, Yunru Bai
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引用次数: 7

Abstract

Question-answering sentiment analysis (QASA) is a novel but meaningful sentiment analysis task based on question-answering online reviews. Existing neural network-based models that conduct sentiment analysis of online reviews have already achieved great success. However, the syntax and implicitly semantic connection in the dependency tree have not been made full use of, especially for Chinese which has specific syntax. In this work, we propose a Residual-Duet Network leveraging textual and tree dependency information for Chinese question-answering sentiment analysis. In particular, we explore the synergies of graph embedding with structural dependency links to learn syntactic information. The transverse and longitudinal compression encoders are developed to capture sentiment evidence with disparate types of compression and different residual connections. We evaluate our model on three Chinese QASA datasets in different domains. Experimental results demonstrate the superiority of our proposed model in Chinese question-answering sentiment analysis.
基于树依赖表示的残差二重网络中文问答情感分析
问答式情感分析(QASA)是一种新颖而有意义的基于问答式在线评论的情感分析任务。现有的基于神经网络的在线评论情感分析模型已经取得了巨大的成功。然而,依赖树中的语法和隐含语义连接并没有得到充分利用,特别是对于具有特定语法的汉语。在这项工作中,我们提出了一种利用文本和树依赖信息的残差二元网络,用于中文问答情感分析。特别是,我们探索了图嵌入与结构依赖链接的协同作用,以学习语法信息。开发了横向和纵向压缩编码器,以捕获具有不同类型压缩和不同残余连接的情感证据。我们在三个不同领域的中国QASA数据集上评估了我们的模型。实验结果证明了该模型在汉语问答情感分析中的优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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