Research Paper Classification and Recommendation System based-on Fine-Tuning BERT

Dipto Biswas, Joon-Min Gil
{"title":"Research Paper Classification and Recommendation System based-on Fine-Tuning BERT","authors":"Dipto Biswas, Joon-Min Gil","doi":"10.1109/IRI58017.2023.00058","DOIUrl":null,"url":null,"abstract":"In this paper, we compare the performance of two popular NLP models, pre-train fine-tuned BERT and BiLSTM with combined CNN, in terms of the classification and recommendation tasks of research papers. We conduct the performance evaluation of these two models with research journal benchmark dataset. Performance results show that the pre-train fine-tuned BERT model is superior to CNN-BiLSTM combined model in terms of classification performance.","PeriodicalId":290818,"journal":{"name":"2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI)","volume":"84 7","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IRI58017.2023.00058","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

In this paper, we compare the performance of two popular NLP models, pre-train fine-tuned BERT and BiLSTM with combined CNN, in terms of the classification and recommendation tasks of research papers. We conduct the performance evaluation of these two models with research journal benchmark dataset. Performance results show that the pre-train fine-tuned BERT model is superior to CNN-BiLSTM combined model in terms of classification performance.
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
基于微调BERT的论文分类推荐系统研究
在本文中,我们比较了两种流行的NLP模型,预训练微调BERT和BiLSTM与组合CNN在研究论文分类和推荐任务方面的性能。我们使用研究期刊基准数据集对这两种模型进行了性能评估。性能结果表明,预训练微调BERT模型在分类性能上优于CNN-BiLSTM组合模型。
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
Research Paper Classification and Recommendation System based-on Fine-Tuning BERT Using BERT to Understand TikTok Users’ ADHD Discussion Enhancing Noisy Binary Search Efficiency through Deep Reinforcement Learning Copyright An Approach to Testing Banking Software Using Metamorphic Relations
×
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