Evaluating the Quality of Automatically Extracted Synonymy Information

A. Kumaran, R. Makin, Vijay Pattisapu, Shaik Sharif, Lucy Vanderwende
{"title":"Evaluating the Quality of Automatically Extracted Synonymy Information","authors":"A. Kumaran, R. Makin, Vijay Pattisapu, Shaik Sharif, Lucy Vanderwende","doi":"10.21248/jlcl.23.2008.100","DOIUrl":null,"url":null,"abstract":"Automatic extraction of semantic information, if successful, offers to languages with little or poor resources, the prospects of creating ontological resources inexpensively, thus providing support for common-sense reasoning applications in those languages. In this paper we explore the automatic extraction of synonymy information from large corpora using two complementary techniques: a generic broad-coverage parser for generation of bits of semantic information, and their synthesis into sets of synonyms using automatic sense-disambiguation. To validate the quality of the synonymy information thus extracted, we experiment with English, where appropriate semantic resources are already available. We cull synonymy information from a large corpus and compare it against synonymy information available in several standard sources. We present the results of our methodology, both quantitatively and qualitatively, that indicate good quality synonymy information may be extracted automatically from large corpora using the proposed methodology.","PeriodicalId":346957,"journal":{"name":"LDV Forum","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2008-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"LDV Forum","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.21248/jlcl.23.2008.100","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

Automatic extraction of semantic information, if successful, offers to languages with little or poor resources, the prospects of creating ontological resources inexpensively, thus providing support for common-sense reasoning applications in those languages. In this paper we explore the automatic extraction of synonymy information from large corpora using two complementary techniques: a generic broad-coverage parser for generation of bits of semantic information, and their synthesis into sets of synonyms using automatic sense-disambiguation. To validate the quality of the synonymy information thus extracted, we experiment with English, where appropriate semantic resources are already available. We cull synonymy information from a large corpus and compare it against synonymy information available in several standard sources. We present the results of our methodology, both quantitatively and qualitatively, that indicate good quality synonymy information may be extracted automatically from large corpora using the proposed methodology.
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
自动提取同义词信息的质量评价
语义信息的自动提取如果成功,将为资源少或资源差的语言提供低成本创建本体论资源的前景,从而为这些语言中的常识推理应用提供支持。在本文中,我们探索了使用两种互补技术从大型语料库中自动提取同义词信息:一种通用的广泛覆盖的语法分析器,用于生成语义信息位,以及使用自动语义消歧将其合成为同义词集。为了验证这样提取的同义词信息的质量,我们以英语为实验对象,因为已经有适当的语义资源可用。我们从一个大型语料库中挑选同义词信息,并将其与几个标准来源中的同义词信息进行比较。我们提出了我们的方法的结果,无论是定量的还是定性的,都表明使用所提出的方法可以从大型语料库中自动提取高质量的同义词信息。
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
Satzlänge: Definitionen, Häufigkeiten, Modelle (Am Beispiel slowenischer Prosatexte) A hybrid approach to resolve nominal anaphora Evaluating the Quality of Automatically Extracted Synonymy Information OWL ontologies as a resource for discourse parsing An ontology of linguistic annotations
×
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