English2MindMap: An Automated System for MindMap Generation from English Text

Mohamed Elhoseiny, A. Elgammal
{"title":"English2MindMap: An Automated System for MindMap Generation from English Text","authors":"Mohamed Elhoseiny, A. Elgammal","doi":"10.1109/ISM.2012.103","DOIUrl":null,"url":null,"abstract":"Mind Mapping is a well-known technique used in note taking and is known to encourage learning and studying. Besides, Mind Mapping can be a very good way to present knowledge and concepts in a visual form. Unfortunately there is no reliable automated tool that can generate Mind Maps from Natural Language text. This paper fills in this gap by developing the first evaluated automated system that takes a text input and generates a Mind Map visualization out of it. The system also could visualize large text documents in multilevel Mind Maps in which a high level Mind Map node could be expanded into child Mind Maps. The proposed approach involves understanding of the input text converting it into intermediate Detailed Meaning Representation (DMR). The DMR is then visualized with two proposed approaches, Single level or Multiple levels which is convenient for larger text. The generated Mind Maps from both approaches were evaluated based on Human Subject experiments performed on Amazon Mechanical Turk with various parameter settings.","PeriodicalId":282528,"journal":{"name":"2012 IEEE International Symposium on Multimedia","volume":"186 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE International Symposium on Multimedia","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISM.2012.103","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 13

Abstract

Mind Mapping is a well-known technique used in note taking and is known to encourage learning and studying. Besides, Mind Mapping can be a very good way to present knowledge and concepts in a visual form. Unfortunately there is no reliable automated tool that can generate Mind Maps from Natural Language text. This paper fills in this gap by developing the first evaluated automated system that takes a text input and generates a Mind Map visualization out of it. The system also could visualize large text documents in multilevel Mind Maps in which a high level Mind Map node could be expanded into child Mind Maps. The proposed approach involves understanding of the input text converting it into intermediate Detailed Meaning Representation (DMR). The DMR is then visualized with two proposed approaches, Single level or Multiple levels which is convenient for larger text. The generated Mind Maps from both approaches were evaluated based on Human Subject experiments performed on Amazon Mechanical Turk with various parameter settings.
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
一个从英语文本自动生成思维导图的系统
思维导图是一种众所周知的用于记笔记的技巧,它可以鼓励学习和学习。此外,思维导图是一种以视觉形式呈现知识和概念的好方法。不幸的是,没有可靠的自动化工具可以从自然语言文本生成思维导图。本文通过开发第一个经过评估的自动化系统来填补这一空白,该系统接受文本输入并从中生成思维导图可视化。该系统还可以在多层思维导图中可视化大型文本文档,其中高级思维导图节点可以扩展为子思维导图。所提出的方法包括理解输入文本,将其转换为中间的详细含义表示(DMR)。然后用两种建议的方法来可视化DMR,单级或多级,这便于较大的文本。根据Amazon Mechanical Turk上不同参数设置的人类受试者实验,对两种方法生成的思维导图进行评估。
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
Detailed Comparative Analysis of VP8 and H.264 Enhancing the MST-CSS Representation Using Robust Geometric Features, for Efficient Content Based Video Retrieval (CBVR) A Standardized Metadata Set for Annotation of Virtual and Remote Laboratories Using Wavelets and Gaussian Mixture Models for Audio Classification A Data Aware Admission Control Technique for Social Live Streams (SOLISs)
×
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