一个从电子邮件库自动生成常见问题的框架

Shiney Jeyaraj, Raghuveera Tripuraribhatla
{"title":"一个从电子邮件库自动生成常见问题的框架","authors":"Shiney Jeyaraj, Raghuveera Tripuraribhatla","doi":"10.1109/IEMCON.2018.8614894","DOIUrl":null,"url":null,"abstract":"In many organizations, enquiry emails from customers remain unanswered due to lack of patience and availability of a respondent. Generating FAQs from email repositories with lot of enquiry emails will be beneficial. However, manual generation of FAQs by experts is a time consuming and strenous job. Hence automatic generation of FAQs is a necessity. Automatic generation of FAQs require effective categorization of emails which is challenging since the emails are written by different people with heterogenous cognition levels. In this paper, we propose a framework using Non-negative Matrix Factorization (NMF) and k-means that groups emails into clusters which can be used for FAQ generation. The proposed framework determines not only the broad topic under which the emails have to be tagged but also categorizes the emails into clusters with similar sub contents. The number of clusters was determined by the elbow method whereas the number of topics was fixed by calculating the percentage of relevant topics. The average Silhouette coefficient score of the resulting clusters was found to be 0.52 indicating reasonably good clusters. Also, the Silhouette coefficient score of the proposed method increased by 36.82 % compared to k-means.","PeriodicalId":368939,"journal":{"name":"2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A Framework for Automatic Generation of FAQs from Email Repositories\",\"authors\":\"Shiney Jeyaraj, Raghuveera Tripuraribhatla\",\"doi\":\"10.1109/IEMCON.2018.8614894\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In many organizations, enquiry emails from customers remain unanswered due to lack of patience and availability of a respondent. Generating FAQs from email repositories with lot of enquiry emails will be beneficial. However, manual generation of FAQs by experts is a time consuming and strenous job. Hence automatic generation of FAQs is a necessity. Automatic generation of FAQs require effective categorization of emails which is challenging since the emails are written by different people with heterogenous cognition levels. In this paper, we propose a framework using Non-negative Matrix Factorization (NMF) and k-means that groups emails into clusters which can be used for FAQ generation. The proposed framework determines not only the broad topic under which the emails have to be tagged but also categorizes the emails into clusters with similar sub contents. The number of clusters was determined by the elbow method whereas the number of topics was fixed by calculating the percentage of relevant topics. The average Silhouette coefficient score of the resulting clusters was found to be 0.52 indicating reasonably good clusters. Also, the Silhouette coefficient score of the proposed method increased by 36.82 % compared to k-means.\",\"PeriodicalId\":368939,\"journal\":{\"name\":\"2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)\",\"volume\":\"78 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IEMCON.2018.8614894\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IEMCON.2018.8614894","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

在许多组织中,由于缺乏耐心和回复的可用性,来自客户的询问电子邮件仍然没有得到答复。从包含大量查询电子邮件的电子邮件库中生成常见问题解答将是有益的。然而,由专家手动生成faq是一项耗时且费力的工作。因此,自动生成常见问题解答是必要的。自动生成faq需要对电子邮件进行有效的分类,这是一个挑战,因为电子邮件是由不同的认知水平不同的人写的。在本文中,我们提出了一个使用非负矩阵分解(NMF)和k-means的框架,该框架将电子邮件分组成可用于FAQ生成的簇。该框架不仅确定了电子邮件需要标记的主题,而且还将电子邮件分类为具有相似子内容的簇。聚类数采用肘部法确定,主题数通过计算相关主题的百分比确定。所得到的聚类的平均剪影系数得分为0.52,表明聚类相当好。与k-means相比,该方法的廓形系数得分提高了36.82%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
A Framework for Automatic Generation of FAQs from Email Repositories
In many organizations, enquiry emails from customers remain unanswered due to lack of patience and availability of a respondent. Generating FAQs from email repositories with lot of enquiry emails will be beneficial. However, manual generation of FAQs by experts is a time consuming and strenous job. Hence automatic generation of FAQs is a necessity. Automatic generation of FAQs require effective categorization of emails which is challenging since the emails are written by different people with heterogenous cognition levels. In this paper, we propose a framework using Non-negative Matrix Factorization (NMF) and k-means that groups emails into clusters which can be used for FAQ generation. The proposed framework determines not only the broad topic under which the emails have to be tagged but also categorizes the emails into clusters with similar sub contents. The number of clusters was determined by the elbow method whereas the number of topics was fixed by calculating the percentage of relevant topics. The average Silhouette coefficient score of the resulting clusters was found to be 0.52 indicating reasonably good clusters. Also, the Silhouette coefficient score of the proposed method increased by 36.82 % compared to k-means.
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
期刊最新文献
On the Fog Node Model for Multi-purpose Fog Computing Systems Research-Practice Gap in Passive House Standard Propagation Modeling of IoT Devices for Deployment in Multi-level Hilly Urban Environments Architectures and Challenges Towards Software Defined Cloud of Things (SDCoT) Unveiling Topics from Scientific Literature on the Subject of Self-driving Cars using Latent Dirichlet Allocation
×
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