{"title":"Natural language call routing: towards combination and boosting of classifiers","authors":"I. Zitouni, H. Kuo, Chin-Hui Lee","doi":"10.1109/ASRU.2001.1034622","DOIUrl":null,"url":null,"abstract":"We describe different techniques to improve natural language call routing: boosting, relevance feedback, discriminative training, and constrained minimization. Their common goal is to reweight the data in order to let the system focus on documents judged hard to classify by a single classifier. These approaches are evaluated with the common vector-based classifier and also with the beta classifier which had given good results in the similar task of E-mail steering. We explore ways of deriving and combining uncorrelated classifiers in order to improve accuracy. Compared to the cosine and beta baseline classifiers, we report an improvement of 49% and 10%, respectively.","PeriodicalId":118671,"journal":{"name":"IEEE Workshop on Automatic Speech Recognition and Understanding, 2001. ASRU '01.","volume":"139 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2001-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Workshop on Automatic Speech Recognition and Understanding, 2001. ASRU '01.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ASRU.2001.1034622","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
We describe different techniques to improve natural language call routing: boosting, relevance feedback, discriminative training, and constrained minimization. Their common goal is to reweight the data in order to let the system focus on documents judged hard to classify by a single classifier. These approaches are evaluated with the common vector-based classifier and also with the beta classifier which had given good results in the similar task of E-mail steering. We explore ways of deriving and combining uncorrelated classifiers in order to improve accuracy. Compared to the cosine and beta baseline classifiers, we report an improvement of 49% and 10%, respectively.
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