基于深度神经网络的增强作者身份识别超参数整定

Tarun Kumar Dugar, S. Gowtham, U. K. Chakraborty
{"title":"基于深度神经网络的增强作者身份识别超参数整定","authors":"Tarun Kumar Dugar, S. Gowtham, U. K. Chakraborty","doi":"10.1109/ICOEI.2019.8862631","DOIUrl":null,"url":null,"abstract":"Authorship Identification as a task has been long studied and explored. Historically, authorship claims were ratified for copyright issues after the death of the author for unpublished work through style matching. The immense growth in the reach of internet technologies has once again brought to the fore the importance of authorship identification. An application opening up in areas like Intellectual Property Right settlement, Copyrights, Plagiarism, Cyber Crime and Forensics, authorship identification is now an area of active research. The current work presents a Deep Neural Network based approach to authorship identification from a large corpus. The experiments carried out bring out the applicability of Deep Neural Networks for the task and also highlights the importance of hyperparameter tuning for the purpose. Results show that a proper choice and balance in the hyperparameter setting can improve already established outcomes.","PeriodicalId":212501,"journal":{"name":"2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Hyperparameter Tuning for Enhanced Authorship Identification Using Deep Neural Networks\",\"authors\":\"Tarun Kumar Dugar, S. Gowtham, U. K. Chakraborty\",\"doi\":\"10.1109/ICOEI.2019.8862631\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Authorship Identification as a task has been long studied and explored. Historically, authorship claims were ratified for copyright issues after the death of the author for unpublished work through style matching. The immense growth in the reach of internet technologies has once again brought to the fore the importance of authorship identification. An application opening up in areas like Intellectual Property Right settlement, Copyrights, Plagiarism, Cyber Crime and Forensics, authorship identification is now an area of active research. The current work presents a Deep Neural Network based approach to authorship identification from a large corpus. The experiments carried out bring out the applicability of Deep Neural Networks for the task and also highlights the importance of hyperparameter tuning for the purpose. Results show that a proper choice and balance in the hyperparameter setting can improve already established outcomes.\",\"PeriodicalId\":212501,\"journal\":{\"name\":\"2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)\",\"volume\":\"15 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICOEI.2019.8862631\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICOEI.2019.8862631","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

作者身份鉴定作为一项研究和探索已久。从历史上看,作者身份声明是在作者去世后通过风格匹配的方式对未发表的作品进行版权认证的。互联网技术覆盖面的巨大增长再次凸显了作者身份识别的重要性。在知识产权结算、版权、剽窃、网络犯罪和取证等领域的应用程序开放,作者身份鉴定现在是一个活跃的研究领域。目前的工作提出了一种基于深度神经网络的方法来识别大型语料库的作者身份。实验结果表明了深度神经网络对该任务的适用性,并强调了超参数整定的重要性。结果表明,在超参数设置中适当的选择和平衡可以改善已有的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hyperparameter Tuning for Enhanced Authorship Identification Using Deep Neural Networks
Authorship Identification as a task has been long studied and explored. Historically, authorship claims were ratified for copyright issues after the death of the author for unpublished work through style matching. The immense growth in the reach of internet technologies has once again brought to the fore the importance of authorship identification. An application opening up in areas like Intellectual Property Right settlement, Copyrights, Plagiarism, Cyber Crime and Forensics, authorship identification is now an area of active research. The current work presents a Deep Neural Network based approach to authorship identification from a large corpus. The experiments carried out bring out the applicability of Deep Neural Networks for the task and also highlights the importance of hyperparameter tuning for the purpose. Results show that a proper choice and balance in the hyperparameter setting can improve already established outcomes.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信