{"title":"无冗余视图的弱监督自然语言学习","authors":"Vincent Ng, Claire Cardie","doi":"10.3115/1073445.1073468","DOIUrl":null,"url":null,"abstract":"We investigate single-view algorithms as an alternative to multi-view algorithms for weakly supervised learning for natural language processing tasks without a natural feature split. In particular, we apply co-training, self-training, and EM to one such task and find that both self-training and FS-EM, a new variation of EM that incorporates feature selection, outperform co-training and are comparatively less sensitive to parameter changes.","PeriodicalId":277518,"journal":{"name":"Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - NAACL '03","volume":"172 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2003-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"140","resultStr":"{\"title\":\"Weakly Supervised Natural Language Learning Without Redundant Views\",\"authors\":\"Vincent Ng, Claire Cardie\",\"doi\":\"10.3115/1073445.1073468\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We investigate single-view algorithms as an alternative to multi-view algorithms for weakly supervised learning for natural language processing tasks without a natural feature split. In particular, we apply co-training, self-training, and EM to one such task and find that both self-training and FS-EM, a new variation of EM that incorporates feature selection, outperform co-training and are comparatively less sensitive to parameter changes.\",\"PeriodicalId\":277518,\"journal\":{\"name\":\"Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - NAACL '03\",\"volume\":\"172 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2003-05-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"140\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - NAACL '03\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3115/1073445.1073468\",\"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 the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - NAACL '03","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3115/1073445.1073468","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Weakly Supervised Natural Language Learning Without Redundant Views
We investigate single-view algorithms as an alternative to multi-view algorithms for weakly supervised learning for natural language processing tasks without a natural feature split. In particular, we apply co-training, self-training, and EM to one such task and find that both self-training and FS-EM, a new variation of EM that incorporates feature selection, outperform co-training and are comparatively less sensitive to parameter changes.
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