计算机科学 ›› 2022, Vol. 49 ›› Issue (3): 246-254.doi: 10.11896/jsjkx.201200073
李浩, 张兰, 杨兵, 杨海潇, 寇勇奇, 王飞, 康雁
LI Hao, ZHANG Lan, YANG Bing, YANG Hai-xiao, KOU Yong-qi, WANG Fei, KANG Yan
摘要： 利用深度学习模型和注意力机制对微博文本进行细粒度情感分类,已成为研究的热点,但是现有注意力机制只考虑单词对单词的影响,对单词本身的多种维度特性(如词义、词性、语义等特征信息)缺乏有效的融合。为了解决这个问题,文中提出了一种双重权重机制WDWM(Word and Dimension Weight Mechanism),并将其与基于解析依赖树的GCN模型相结合,通过选择每条微博中含有关键信息的单词,来抽取单词的重要维度特性,对单词的多种维度特性进行有效融合,从而捕获更加丰富的特征信息。在针对微博细粒度情感分类的实验中,融合双重权重机制和图卷积神经网络的微博细粒度情感分类模型(WDWM-GCN)的F测度达到了84.02%,比2020年提出的最新的算法高出1.7%,这进一步证明,WDWM-GCN能对单词的多维度特性进行有效的融合,能够捕获丰富的特征信息。在对搜狗新闻数据集进行分类的实验中,BERT模型在加入WDWM后,其分类效果得到了进一步提升,这充分证明 WDWM对所提分类模型有明显的改进效果。
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