Knowledge Commons of Institute of Automation,CAS
Identifying Topic and Cause for Sarcasm An Unsupervised Knowledge-enhanced Prompt Method | |
Minjie, Yuan1,2; Qiudan, Li1; Xue, Mao1,2; Daniel Dajun, Zeng1,2 | |
2023-04 | |
会议名称 | Proceedings of the Web conference 2023 |
会议录名称 | WWW’23 Companion |
页码 | 184–187 |
会议日期 | 2023-4 |
会议地点 | Austin, TX, USA |
会议录编者/会议主办者 | Association for Computing Machinery |
出版地 | New York, NY, USA |
出版者 | Association for Computing Machinery |
产权排序 | 1 |
摘要 | Sarcasm is usually emotional and topical. Mining the characteristics of sarcasm semantics in different emotional tendencies and topic expressions helps gain insight into the sarcasm cause. Most of the existing work detect sarcasm or topic label based on a supervised learning framework, which requires heavy data annotation work. To overcome the above challenges, inspired by the multi-task learning framework, this paper proposes an unsupervised knowledge-enhanced prompt method. This method uses the similarity interaction mechanism to mine the hidden relationship between the sarcasm cause and topic, which integrates external knowledge, such as syntax and emotion, into the prompting and generation process. Additionally, it identifies the sarcasm cause and topic simultaneously. Experimental results on a real-world dataset verify the effectiveness of the proposed model. |
收录类别 | EI |
语种 | 英语 |
七大方向——子方向分类 | 社会计算 |
国重实验室规划方向分类 | 社会信息感知与理解 |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/56576 |
专题 | 舆论大数据科学与技术应用联合实验室 |
通讯作者 | Qiudan, Li |
作者单位 | 1.State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences 2.School of Artificial Intelligence, University of Chinese Academy of Sciences |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Minjie, Yuan,Qiudan, Li,Xue, Mao,et al. Identifying Topic and Cause for Sarcasm An Unsupervised Knowledge-enhanced Prompt Method[C]//Association for Computing Machinery. New York, NY, USA:Association for Computing Machinery,2023:184–187. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Identifying Topic an(447KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 |
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