LINYA: Name Entity Recognition Web-based Text Annotation

Jhanika F. Fanlo, Joyce Anne H. Lanceta, Janea Patrizia R. Pascua, Alfonso Louis Philip R. Salas, John Patrick C. To, Ramon L. Rodriguez
{"title":"LINYA: Name Entity Recognition Web-based Text Annotation","authors":"Jhanika F. Fanlo, Joyce Anne H. Lanceta, Janea Patrizia R. Pascua, Alfonso Louis Philip R. Salas, John Patrick C. To, Ramon L. Rodriguez","doi":"10.1109/ICACTE55855.2022.9943629","DOIUrl":null,"url":null,"abstract":"The world was put in disarray when the novel coronavirus first began. Furthermore, when the World Health Organization (WHO) declared the novel coronavirus outbreak a public health emergency of international concern (PHEIC), people prepared safety protocols to minimize the effect of the virus. One of these is the implementation of e-learning in countries, including the Philippines. As this contactless learning began, students’ motivation decreased due to a lack of private space/classroom and face-to-face communication with their teachers. Learners’ motivation is as crucial as this influences their pace to learn. The researchers developed a tool to help students with their studies and motivate them. LINYA is a web-based text annotation tool in machine learning. The tool was developed using an NLP method in machine learning. The researchers used automated Agile testing with four phases in testing the web tool. It began with component testing and progressed to integration, system, and acceptance testing. Based on the results from simulated data, the tests showed favorable results, with mean scores ranging from 3.8 to 4.6, for all areas of a usability test. It further shows that the developed system is ready for implementation.","PeriodicalId":165068,"journal":{"name":"2022 15th International Conference on Advanced Computer Theory and Engineering (ICACTE)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 15th International Conference on Advanced Computer Theory and Engineering (ICACTE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICACTE55855.2022.9943629","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

The world was put in disarray when the novel coronavirus first began. Furthermore, when the World Health Organization (WHO) declared the novel coronavirus outbreak a public health emergency of international concern (PHEIC), people prepared safety protocols to minimize the effect of the virus. One of these is the implementation of e-learning in countries, including the Philippines. As this contactless learning began, students’ motivation decreased due to a lack of private space/classroom and face-to-face communication with their teachers. Learners’ motivation is as crucial as this influences their pace to learn. The researchers developed a tool to help students with their studies and motivate them. LINYA is a web-based text annotation tool in machine learning. The tool was developed using an NLP method in machine learning. The researchers used automated Agile testing with four phases in testing the web tool. It began with component testing and progressed to integration, system, and acceptance testing. Based on the results from simulated data, the tests showed favorable results, with mean scores ranging from 3.8 to 4.6, for all areas of a usability test. It further shows that the developed system is ready for implementation.
名称实体识别基于网络的文本注释
当新型冠状病毒首次出现时,世界陷入了混乱。此外,当世界卫生组织(WHO)宣布新型冠状病毒爆发为国际关注的突发公共卫生事件(PHEIC)时,人们制定了安全方案,以尽量减少病毒的影响。其中之一是在包括菲律宾在内的国家实施电子学习。随着这种非接触式学习的开始,由于缺乏私人空间/教室和与老师面对面的交流,学生的学习动机下降了。学习者的动机同样重要,因为这会影响他们的学习速度。研究人员开发了一种工具来帮助学生学习并激励他们。LINYA是一个基于web的机器学习文本注释工具。该工具是使用机器学习中的NLP方法开发的。研究人员在测试web工具时使用了四个阶段的自动化敏捷测试。它从组件测试开始,并发展到集成、系统和验收测试。根据模拟数据的结果,测试显示出良好的结果,可用性测试的所有领域的平均得分从3.8到4.6不等。进一步表明所开发的系统已经可以实现。
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 求助全文
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