{"title":"Deeplearning Model Used in Text Classification","authors":"Jingjing Cai, Jianping Li, Wei Li, Ji Wang","doi":"10.1109/ICCWAMTIP.2018.8632592","DOIUrl":null,"url":null,"abstract":"Text classification is one of the most widely used natural language processing technologies. Common text classification applications include spam identification, news text classification, information retrieval, emotion analysis, and intention judgment, etc. Traditional text classifiers based on machine learning methods have defects such as data sparsity, dimension explosion and poor generalization ability, while classifiers based on deep learning network greatly improve these defects, avoid cumbersome feature extraction process, and have strong learning ability and higher prediction accuracy. For example, convolutional neural network (CNN)[I]. This paper introduces the process of text classification and focuses on the deep learning model used in text classification.","PeriodicalId":117919,"journal":{"name":"2018 15th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"106 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"43","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 15th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCWAMTIP.2018.8632592","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 43
Abstract
Text classification is one of the most widely used natural language processing technologies. Common text classification applications include spam identification, news text classification, information retrieval, emotion analysis, and intention judgment, etc. Traditional text classifiers based on machine learning methods have defects such as data sparsity, dimension explosion and poor generalization ability, while classifiers based on deep learning network greatly improve these defects, avoid cumbersome feature extraction process, and have strong learning ability and higher prediction accuracy. For example, convolutional neural network (CNN)[I]. This paper introduces the process of text classification and focuses on the deep learning model used in text classification.