College of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China.
Institute of Cognitive and Intelligent Computing, Hangzhou Dianzi University, Hangzhou 310018, China.
Sensors (Basel). 2022 Jul 23;22(15):5509. doi: 10.3390/s22155509.
Humor is a special human expression style, an important "lubricant" for daily communication for people; people can convey emotional messages that are not easily expressed through humor. At present, artificial intelligence is one of the popular research domains; "discourse understanding" is also an important research direction, and how to make computers recognize and understand humorous expressions similar to humans has become one of the popular research domains for natural language processing researchers. In this paper, a humor recognition model (MLSN) based on current humor theory and popular deep learning techniques is proposed for the humor recognition task. The model automatically identifies whether a sentence contains humor expression by capturing the inconsistency, phonetic features, and ambiguity of a joke as semantic features. The model was experimented on three publicly available wisecrack datasets and compared with state-of-the-art language models, and the results demonstrate that the proposed model has better humor recognition accuracy and can contribute to the research on discourse understanding.
幽默是一种特殊的人类表达方式，是人们日常交流的重要“润滑剂”；人们可以通过幽默来传达那些不易用语言表达的情感信息。目前，人工智能是热门研究领域之一；“话语理解”也是一个重要的研究方向，如何使计算机像人类一样识别和理解类似幽默的表达方式已成为自然语言处理研究人员的热门研究领域之一。本文针对幽默识别任务，提出了一种基于当前幽默理论和流行的深度学习技术的幽默识别模型（MLSN）。该模型通过捕捉笑话的不一致性、语音特征和歧义等语义特征，自动识别句子是否包含幽默表达。该模型在三个公开的俏皮话数据集上进行了实验，并与最先进的语言模型进行了比较，结果表明，所提出的模型具有更好的幽默识别准确性，可以为话语理解的研究做出贡献。
