{"title":"利用上下文向量和反向传播算法提高自动文本处理系统的分类精度","authors":"J. Farkas","doi":"10.1109/CCECE.1996.548248","DOIUrl":null,"url":null,"abstract":"We analyze some of the benefits of combining the context-vector representation of documents with the back-propagation paradigm for document classification. We discuss an implementation of this architecture, called NeuroFile, which combines automatic document classification with similarity-based, as well as Boolean retrieval facilities in a single electronic filing system. The quality of performance of NeuroFile is compared with an earlier system called NeuroClass. We show that NeuroFile achieves a 9% classification improvement over NeuroClass.","PeriodicalId":269440,"journal":{"name":"Proceedings of 1996 Canadian Conference on Electrical and Computer Engineering","volume":"215 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1996-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Improving the classification accuracy of automatic text processing systems using context vectors and back-propagation algorithms\",\"authors\":\"J. Farkas\",\"doi\":\"10.1109/CCECE.1996.548248\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We analyze some of the benefits of combining the context-vector representation of documents with the back-propagation paradigm for document classification. We discuss an implementation of this architecture, called NeuroFile, which combines automatic document classification with similarity-based, as well as Boolean retrieval facilities in a single electronic filing system. The quality of performance of NeuroFile is compared with an earlier system called NeuroClass. We show that NeuroFile achieves a 9% classification improvement over NeuroClass.\",\"PeriodicalId\":269440,\"journal\":{\"name\":\"Proceedings of 1996 Canadian Conference on Electrical and Computer Engineering\",\"volume\":\"215 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1996-05-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of 1996 Canadian Conference on Electrical and Computer Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CCECE.1996.548248\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of 1996 Canadian Conference on Electrical and Computer Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CCECE.1996.548248","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Improving the classification accuracy of automatic text processing systems using context vectors and back-propagation algorithms
We analyze some of the benefits of combining the context-vector representation of documents with the back-propagation paradigm for document classification. We discuss an implementation of this architecture, called NeuroFile, which combines automatic document classification with similarity-based, as well as Boolean retrieval facilities in a single electronic filing system. The quality of performance of NeuroFile is compared with an earlier system called NeuroClass. We show that NeuroFile achieves a 9% classification improvement over NeuroClass.