计算机科学 ›› 2023, Vol. 50 ›› Issue (4): 196-203.doi: 10.11896/jsjkx.220100105
王娅丽1, 张凡1,2, 余增1,2, 李天瑞1,2
WANG Yali1, ZHANG Fan1,2, YU Zeng1,2, LI Tianrui1,2
摘要: 方面级情感分析是细粒度情感分析中的一项关键任务,旨在预测一个句子中不同方面术语的情感倾向。针对目前结合图卷积网络的研究忽略方面术语本身的含义以及方面术语与上下文之间的交互的问题,文中提出了基于交互注意力和图卷积网络的模型(Interactive Attention Graph Convolution Network,IAGCN)。该模型首先结合BiLSTM和修正动态权重层对上下文进行建模,其次在句法依存树上使用图卷积网络对句法信息进行编码,然后利用交互注意力机制学习上下文和方面术语中的注意力,重构上下文和方面术语的表示,最后通过softmax层获取给定方面术语的情感极性。与基线模型相比,所提模型在5个数据集中的准确率和F1值分别提高了0.56%~1.75%和1.34%~4.04%。同时,将预训练模型BERT应用到此任务中,相比基于GloVe的IAGCN模型,其准确率和F1值分别提高了1.47%~3.95%和2.59%~7.55%,模型效果有了进一步的提升。
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