基于深度神经网络的词嵌入技术释义检测

Veena Gangadharan, Deepa Gupta, Amritha L, Athira T A
{"title":"基于深度神经网络的词嵌入技术释义检测","authors":"Veena Gangadharan, Deepa Gupta, Amritha L, Athira T A","doi":"10.1109/ICOEI48184.2020.9142877","DOIUrl":null,"url":null,"abstract":"This paper focuses on detecting paraphrase in sentences using different word vectorization techniques and finding which vectorization method is more efficient. Word vectorization is a technique which is used to retrieve information from large collection of textual data like corpus or documents by associating each word as a vector. As the textual data are massive, the problem with the text data is that it need to defined in a form of numbers for solving mathematical problems. There are elementary to composite methods to solve this problem. In this paper we are comparing different word vectorization techniques and they are, Count Vectorizer,Hashing Vectorizer, TF-IDF Vectorizer, fastText, ELMo, GloVe, BERT.","PeriodicalId":267795,"journal":{"name":"2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":"{\"title\":\"Paraphrase Detection Using Deep Neural Network Based Word Embedding Techniques\",\"authors\":\"Veena Gangadharan, Deepa Gupta, Amritha L, Athira T A\",\"doi\":\"10.1109/ICOEI48184.2020.9142877\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper focuses on detecting paraphrase in sentences using different word vectorization techniques and finding which vectorization method is more efficient. Word vectorization is a technique which is used to retrieve information from large collection of textual data like corpus or documents by associating each word as a vector. As the textual data are massive, the problem with the text data is that it need to defined in a form of numbers for solving mathematical problems. There are elementary to composite methods to solve this problem. In this paper we are comparing different word vectorization techniques and they are, Count Vectorizer,Hashing Vectorizer, TF-IDF Vectorizer, fastText, ELMo, GloVe, BERT.\",\"PeriodicalId\":267795,\"journal\":{\"name\":\"2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184)\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"13\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICOEI48184.2020.9142877\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICOEI48184.2020.9142877","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 13

摘要

本文主要研究了使用不同的词向量化技术来检测句子中的释义,并找出哪种向量化方法更有效。词向量化是一种通过将每个词作为向量关联来从语料库或文档等大量文本数据中检索信息的技术。由于文本数据非常庞大,因此文本数据的问题在于需要将其定义为数字形式以解决数学问题。解决这一问题有基本的或综合的方法。在本文中,我们比较了不同的词矢量化技术,它们是计数矢量化,哈希矢量化,TF-IDF矢量化,fastText, ELMo, GloVe, BERT。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Paraphrase Detection Using Deep Neural Network Based Word Embedding Techniques
This paper focuses on detecting paraphrase in sentences using different word vectorization techniques and finding which vectorization method is more efficient. Word vectorization is a technique which is used to retrieve information from large collection of textual data like corpus or documents by associating each word as a vector. As the textual data are massive, the problem with the text data is that it need to defined in a form of numbers for solving mathematical problems. There are elementary to composite methods to solve this problem. In this paper we are comparing different word vectorization techniques and they are, Count Vectorizer,Hashing Vectorizer, TF-IDF Vectorizer, fastText, ELMo, GloVe, BERT.
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
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信