Deep-SHEEP: Sense of Humor Extraction from Embeddings in the Personalized Context

Julita Bielaniewicz, Kamil Kanclerz, P. Milkowski, Marcin Gruza, Konrad Karanowski, Przemyslaw Kazienko, Jan Kocoń
{"title":"Deep-SHEEP: Sense of Humor Extraction from Embeddings in the Personalized Context","authors":"Julita Bielaniewicz, Kamil Kanclerz, P. Milkowski, Marcin Gruza, Konrad Karanowski, Przemyslaw Kazienko, Jan Kocoń","doi":"10.1109/ICDMW58026.2022.00125","DOIUrl":null,"url":null,"abstract":"As humans, we experience a wide range of feelings and reactions. One of these is laughter, often related to a personal sense of humor and the perception of funny content. Due to its subjective nature, recognizing humor in NLP is a very challenging task. Here, we present a new approach to the task of predicting humor in the text by applying the idea of a personalized approach. It takes into account both the text and the context of the content receiver. For that purpose, we proposed four Deep-SHEEP learning models that take advantage of user preference information differently. The experiments were conducted on four datasets: Cockamamie, HUMOR, Jester, and Humicroedit. The results have shown that the application of an innovative personalized approach and user-centric perspective significantly improves performance compared to generalized methods. Moreover, even for random text embeddings, our personalized methods outperform the generalized ones in the subjective humor modeling task. We also argue that the user-related data reflecting an individual sense of humor has similar importance as the evaluated text itself. Different types of humor were investigated as well.","PeriodicalId":146687,"journal":{"name":"2022 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE International Conference on Data Mining Workshops (ICDMW)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDMW58026.2022.00125","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5

Abstract

As humans, we experience a wide range of feelings and reactions. One of these is laughter, often related to a personal sense of humor and the perception of funny content. Due to its subjective nature, recognizing humor in NLP is a very challenging task. Here, we present a new approach to the task of predicting humor in the text by applying the idea of a personalized approach. It takes into account both the text and the context of the content receiver. For that purpose, we proposed four Deep-SHEEP learning models that take advantage of user preference information differently. The experiments were conducted on four datasets: Cockamamie, HUMOR, Jester, and Humicroedit. The results have shown that the application of an innovative personalized approach and user-centric perspective significantly improves performance compared to generalized methods. Moreover, even for random text embeddings, our personalized methods outperform the generalized ones in the subjective humor modeling task. We also argue that the user-related data reflecting an individual sense of humor has similar importance as the evaluated text itself. Different types of humor were investigated as well.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Deep-SHEEP:个性化语境下嵌入的幽默感提取
作为人类,我们会经历各种各样的感受和反应。其中之一是笑,通常与个人幽默感和对有趣内容的感知有关。由于其主观性,识别NLP中的幽默是一项非常具有挑战性的任务。在这里,我们提出了一种新的方法,通过应用个性化的方法来预测文本中的幽默。它同时考虑到文本和内容接收者的上下文。为此,我们提出了四种不同利用用户偏好信息的Deep-SHEEP学习模型。实验在四个数据集上进行:Cockamamie, HUMOR, Jester和Humicroedit。结果表明,与通用方法相比,创新的个性化方法和以用户为中心的观点的应用显着提高了性能。此外,即使对于随机文本嵌入,我们的个性化方法在主观幽默建模任务中也优于广义方法。我们还认为,反映个人幽默感的用户相关数据与评估文本本身具有相似的重要性。不同类型的幽默也被调查。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Above Ground Biomass Estimation of a Cocoa Plantation using Machine Learning Backdoor Poisoning of Encrypted Traffic Classifiers Identifying Patterns of Vulnerability Incidence in Foundational Machine Learning Repositories on GitHub: An Unsupervised Graph Embedding Approach Data-driven Kernel Subspace Clustering with Local Manifold Preservation Persona-Based Conversational AI: State of the Art and Challenges
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1