Extracting semantic relations using syntax

Kasper Welbers, W. Atteveldt, J. Kleinnijenhuis
{"title":"Extracting semantic relations using syntax","authors":"Kasper Welbers, W. Atteveldt, J. Kleinnijenhuis","doi":"10.5117/ccr2021.2.003.welb","DOIUrl":null,"url":null,"abstract":"\n Most common methods for automatic text analysis in communication science ignore syntactic information, focusing on the occurrence and co-occurrence of individual words, and sometimes n-grams. This is remarkably effective for some purposes, but poses a limitation for fine-grained analyses into semantic relations such as who does what to whom and according to what source. One tested, effective method for moving beyond this bag-of-words assumption is to use a rule-based approach for labeling and extracting syntactic patterns in dependency trees. Although this method can be used for a variety of purposes, its application is hindered by the lack of dedicated and accessible tools. In this paper we introduce the rsyntax R package, which is designed to make working with dependency trees easier and more intuitive for R users, and provides a framework for combining multiple rules for reliably extracting useful semantic relations.","PeriodicalId":275035,"journal":{"name":"Computational Communication Research","volume":"64 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational Communication Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5117/ccr2021.2.003.welb","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

Abstract

Most common methods for automatic text analysis in communication science ignore syntactic information, focusing on the occurrence and co-occurrence of individual words, and sometimes n-grams. This is remarkably effective for some purposes, but poses a limitation for fine-grained analyses into semantic relations such as who does what to whom and according to what source. One tested, effective method for moving beyond this bag-of-words assumption is to use a rule-based approach for labeling and extracting syntactic patterns in dependency trees. Although this method can be used for a variety of purposes, its application is hindered by the lack of dedicated and accessible tools. In this paper we introduce the rsyntax R package, which is designed to make working with dependency trees easier and more intuitive for R users, and provides a framework for combining multiple rules for reliably extracting useful semantic relations.
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
使用语法提取语义关系
在通信科学中,大多数常用的自动文本分析方法都忽略了句法信息,只关注单个单词的出现和共现,有时也关注n-gram。这对于某些目的来说非常有效,但是对语义关系的细粒度分析(比如谁对谁做什么,根据什么来源做什么)造成了限制。一种经过测试的有效方法可以超越这种词袋假设,即使用基于规则的方法来标记和提取依赖树中的语法模式。虽然这种方法可以用于各种目的,但由于缺乏专用的和可访问的工具,它的应用受到阻碍。在本文中,我们介绍了rsyntax R包,它旨在使R用户更容易和更直观地使用依赖树,并提供了一个框架来组合多个规则,以可靠地提取有用的语义关系。
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
Using State-of-the-art Emotion Detection Models in a Crisis Communication Context How COVID-19 and the News Shaped Populism in Facebook Comments in Seven European Countries. : A Computational Analysis. Agent-based modeling of diversity, new information and minority groups in opinion formation Going Micro to Go Negative? Algorithmic Recommendations’ Role for the Interrelatedness of Counter-Messages and Polluted Content on YouTube – A Network Analysis
×
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