基于多维潜在语义特征的幽默识别

Humor Recognition Based on Latent Semantic Features

幽默是人类独有的品质,在日常交际中发挥着重要作用。随着人工智能的快速发展,如何让计算机识别幽默成了自然语言处理研究领域的热门研究内容之一。该文针对幽默的自动识别问题,基于幽默理论和领域知识,系统地分析总结了幽默的五类显著特性,包括不一致特性、模糊特性、情感特性、语音特性和句法结构特性,并针对每一类特性构建了多种幽默特征。实验结果表明,该文所提出的幽默特征能够从多个角度对幽默的潜在语义表达进行良好的表征,在两个俏皮话类型的幽默数据集上的实验性能均有显著提升。

Humor recognition is a challenge in the field of natural language processing.According to humor theories and cognitive linguistics,five types of distinctive aspects of humor are systematically analyzed,and a variety of humor features are derived.The experiment results show that the proposed features can better represent the latent semantic information of humor.Furthermore,the deep learning can benefit from these features for humor recognition.