{"title":"在自杀遗书中标记情绪:具有异质特征的成本敏感学习。","authors":"Jonathon Read, Erik Velldal, Lilja Ovrelid","doi":"10.4137/BII.S8930","DOIUrl":null,"url":null,"abstract":"<p><p>This paper describes a system developed for Track 2 of the 2011 Medical NLP Challenge on identifying emotions in suicide notes. Our approach involves learning a collection of one-versus-all classifiers, each deciding whether or not a particular label should be assigned to a given sentence. We explore a variety of features types-syntactic, semantic and surface-oriented. Cost-sensitive learning is used for dealing with the issue of class imbalance in the data.</p>","PeriodicalId":88397,"journal":{"name":"Biomedical informatics insights","volume":"5 Suppl. 1","pages":"99-103"},"PeriodicalIF":0.0000,"publicationDate":"2012-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4137/BII.S8930","citationCount":"4","resultStr":"{\"title\":\"Labeling emotions in suicide notes: cost-sensitive learning with heterogeneous features.\",\"authors\":\"Jonathon Read, Erik Velldal, Lilja Ovrelid\",\"doi\":\"10.4137/BII.S8930\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>This paper describes a system developed for Track 2 of the 2011 Medical NLP Challenge on identifying emotions in suicide notes. Our approach involves learning a collection of one-versus-all classifiers, each deciding whether or not a particular label should be assigned to a given sentence. We explore a variety of features types-syntactic, semantic and surface-oriented. Cost-sensitive learning is used for dealing with the issue of class imbalance in the data.</p>\",\"PeriodicalId\":88397,\"journal\":{\"name\":\"Biomedical informatics insights\",\"volume\":\"5 Suppl. 1\",\"pages\":\"99-103\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.4137/BII.S8930\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Biomedical informatics insights\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4137/BII.S8930\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2012/1/30 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biomedical informatics insights","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4137/BII.S8930","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2012/1/30 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
Labeling emotions in suicide notes: cost-sensitive learning with heterogeneous features.
This paper describes a system developed for Track 2 of the 2011 Medical NLP Challenge on identifying emotions in suicide notes. Our approach involves learning a collection of one-versus-all classifiers, each deciding whether or not a particular label should be assigned to a given sentence. We explore a variety of features types-syntactic, semantic and surface-oriented. Cost-sensitive learning is used for dealing with the issue of class imbalance in the data.
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