计算机科学 ›› 2024, Vol. 51 ›› Issue (9): 310-318.doi: 10.11896/jsjkx.230600217
刘其龙, 李弼程, 黄志勇
LIU Qilong, LI Bicheng, HUANG Zhiyong
摘要: 随着社交媒体的发展,越来越多的人在社交平台上发表对热点话题的看法,其中讽刺手法的运用严重影响了社交媒体中情感分析的精度。目前面向话题的讽刺识别研究未同时考虑上下文和常识知识的作用,也忽略了在同一个话题下进行讽刺识别的场景。为此,提出了基于上下文和常识的讽刺识别模型(Sarcasm Detection with Context and Common Sense,CCSD)。首先,模型使用C3KG常识库生成常识文本,并将目标句、话题上下文和常识文本作为预训练BERT模型的输入。其次,使用注意力机制来关注目标句和常识中重要的信息。最后,通过门控机制和特征融合,实现讽刺识别。文中构建了一个面向话题的讽刺识别数据集,以验证模型在特定话题中的有效性。实验结果表明,相比基线模型,新模型的性能更优。
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