有一个聊天与群集,会话引擎查询大表

HILDA '16 Pub Date : 2016-06-26 DOI:10.1145/2939502.2939504
Thibault Sellam, M. Kersten
{"title":"有一个聊天与群集,会话引擎查询大表","authors":"Thibault Sellam, M. Kersten","doi":"10.1145/2939502.2939504","DOIUrl":null,"url":null,"abstract":"Thanks the recent advances of AI and the stellar popularity of messaging apps (e.g., WhatsApp), chatbots are no longer bound to customer support services and computer museums. Indeed, they provide a mighty, lightweight and accessible way to provide services over the Internet. In this paper, we introduce Clustine, a chatbot to help users query large tables through short messages. The main idea is to combine cluster analysis and text generation to compress query results, describe them with natural language and make recommendations. We present the architecture of our system, demonstrate it with two use cases, and present early validation experiments with 12 real datasets to show that its promises are reachable.","PeriodicalId":356971,"journal":{"name":"HILDA '16","volume":"66 2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Have a chat with clustine, conversational engine to query large tables\",\"authors\":\"Thibault Sellam, M. Kersten\",\"doi\":\"10.1145/2939502.2939504\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Thanks the recent advances of AI and the stellar popularity of messaging apps (e.g., WhatsApp), chatbots are no longer bound to customer support services and computer museums. Indeed, they provide a mighty, lightweight and accessible way to provide services over the Internet. In this paper, we introduce Clustine, a chatbot to help users query large tables through short messages. The main idea is to combine cluster analysis and text generation to compress query results, describe them with natural language and make recommendations. We present the architecture of our system, demonstrate it with two use cases, and present early validation experiments with 12 real datasets to show that its promises are reachable.\",\"PeriodicalId\":356971,\"journal\":{\"name\":\"HILDA '16\",\"volume\":\"66 2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-06-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"HILDA '16\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2939502.2939504\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"HILDA '16","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2939502.2939504","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6

摘要

由于最近人工智能的进步和即时通讯应用(如WhatsApp)的流行,聊天机器人不再局限于客户支持服务和计算机博物馆。实际上,它们提供了一种强大的、轻量级的和可访问的方式来通过Internet提供服务。在本文中,我们介绍了一个聊天机器人Clustine,它可以帮助用户通过短消息查询大型表。其主要思想是将聚类分析和文本生成相结合,对查询结果进行压缩,用自然语言进行描述,并提出建议。我们展示了我们系统的架构,用两个用例进行了演示,并用12个真实数据集进行了早期验证实验,以表明它的承诺是可以实现的。
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
Have a chat with clustine, conversational engine to query large tables
Thanks the recent advances of AI and the stellar popularity of messaging apps (e.g., WhatsApp), chatbots are no longer bound to customer support services and computer museums. Indeed, they provide a mighty, lightweight and accessible way to provide services over the Internet. In this paper, we introduce Clustine, a chatbot to help users query large tables through short messages. The main idea is to combine cluster analysis and text generation to compress query results, describe them with natural language and make recommendations. We present the architecture of our system, demonstrate it with two use cases, and present early validation experiments with 12 real datasets to show that its promises are reachable.
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
VisTrees: fast indexes for interactive data exploration PFunk-H: approximate query processing using perceptual models Towards reliable interactive data cleaning: a user survey and recommendations ModelDB: a system for machine learning model management TrendQuery: a system for interactive exploration of trends
×
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