基于字词特征自注意学习的社交媒体文本分类方法

Q4 Computer Science
王晓莉, 叶东毅
{"title":"基于字词特征自注意学习的社交媒体文本分类方法","authors":"王晓莉, 叶东毅","doi":"10.16451/J.CNKI.ISSN1003-6059.202004001","DOIUrl":null,"url":null,"abstract":"Long tail effect and excessive out-of-vocabulary(OOV)words in social media texts result in severe feature sparsity and reduce classification accuracy.To solve the problem,a social media text classification method based on character-word feature self-attention learning is proposed.Global features are constructed at the character level to learn attention weight distribution,and the existing multi-head attention mechanism is improved to reduce parameter scale and computational complexity.To further analyze character-word feature fusion,OOV sensitivity is proposed to measure the impact of OOV words on different types of features.Experiments on several social media text classification tasks indicate that the effectiveness and classification accuracy of the proposed method are obviously improved in terms of fusing word features and character features.Moreover,the quantitative results of OOV vocabulary sensitivity index verify the feasiblity and effectiveness of the proposed method.","PeriodicalId":34917,"journal":{"name":"模式识别与人工智能","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Social Media Text Classification Method Based on Character-Word Feature Self-attention Learning\",\"authors\":\"王晓莉, 叶东毅\",\"doi\":\"10.16451/J.CNKI.ISSN1003-6059.202004001\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Long tail effect and excessive out-of-vocabulary(OOV)words in social media texts result in severe feature sparsity and reduce classification accuracy.To solve the problem,a social media text classification method based on character-word feature self-attention learning is proposed.Global features are constructed at the character level to learn attention weight distribution,and the existing multi-head attention mechanism is improved to reduce parameter scale and computational complexity.To further analyze character-word feature fusion,OOV sensitivity is proposed to measure the impact of OOV words on different types of features.Experiments on several social media text classification tasks indicate that the effectiveness and classification accuracy of the proposed method are obviously improved in terms of fusing word features and character features.Moreover,the quantitative results of OOV vocabulary sensitivity index verify the feasiblity and effectiveness of the proposed method.\",\"PeriodicalId\":34917,\"journal\":{\"name\":\"模式识别与人工智能\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"模式识别与人工智能\",\"FirstCategoryId\":\"1093\",\"ListUrlMain\":\"https://doi.org/10.16451/J.CNKI.ISSN1003-6059.202004001\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"Computer Science\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"模式识别与人工智能","FirstCategoryId":"1093","ListUrlMain":"https://doi.org/10.16451/J.CNKI.ISSN1003-6059.202004001","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Computer Science","Score":null,"Total":0}
引用次数: 0

摘要

社交媒体文本中的长尾效应和过度的词汇外(OOV)导致了严重的特征稀疏性,降低了分类精度。为了解决这一问题,提出了一种基于特征词自注意学习的社交媒体文本分类方法。在字符级别构建全局特征以学习注意力权重分布,并改进现有的多头注意力机制以降低参数规模和计算复杂度。为了进一步分析字-词-特征融合,提出了OOV敏感性来衡量OOV词对不同类型特征的影响。在几个社交媒体文本分类任务上的实验表明,该方法在融合单词特征和字符特征方面,显著提高了分类的有效性和准确性。此外,OOV词汇敏感性指数的定量结果验证了该方法的可行性和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Social Media Text Classification Method Based on Character-Word Feature Self-attention Learning
Long tail effect and excessive out-of-vocabulary(OOV)words in social media texts result in severe feature sparsity and reduce classification accuracy.To solve the problem,a social media text classification method based on character-word feature self-attention learning is proposed.Global features are constructed at the character level to learn attention weight distribution,and the existing multi-head attention mechanism is improved to reduce parameter scale and computational complexity.To further analyze character-word feature fusion,OOV sensitivity is proposed to measure the impact of OOV words on different types of features.Experiments on several social media text classification tasks indicate that the effectiveness and classification accuracy of the proposed method are obviously improved in terms of fusing word features and character features.Moreover,the quantitative results of OOV vocabulary sensitivity index verify the feasiblity and effectiveness of the proposed method.
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
模式识别与人工智能
模式识别与人工智能 Computer Science-Artificial Intelligence
CiteScore
1.60
自引率
0.00%
发文量
3316
期刊介绍:
×
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