Natural Language Call Routing: Towards Combination And Boosting Of Classifiers
ASRU 2001: IEEE WORKSHOP ON AUTOMATIC SPEECH RECOGNITION AND UNDERSTANDING, CONFERENCE PROCEEDINGS（2001）
摘要
In this paper, 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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关键词
call centres,interpolation,learning (artificial intelligence),minimisation,natural language interfaces,pattern classification,relevance feedback,speech recognition,text analysis,asr,e-mail steering,beta classifier,call center,classifier boosting,constrained minimization,cosine classifier,discriminative training,document routing,linear interpolation,natural language call routing,topic identification systems,boosting,information retrieval,feedback,routing,frequency,natural languages,training data,natural language,learning artificial intelligence
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