自然语言调用路由:面向分类器的组合与提升

IEEE Workshop on Automatic Speech Recognition and Understanding, 2001. ASRU '01. Pub Date : 2001-12-09 DOI:10.1109/ASRU.2001.1034622

I. Zitouni, H. Kuo, Chin-Hui Lee

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引用次数: 5

摘要

我们描述了改进自然语言呼叫路由的不同技术:增强、相关反馈、判别训练和约束最小化。他们的共同目标是重新加权数据，以便让系统专注于被单一分类器判断为难以分类的文档。用基于向量的通用分类器和beta分类器对这些方法进行了评估，beta分类器在类似的电子邮件引导任务中取得了很好的效果。为了提高准确率，我们探索了派生和组合不相关分类器的方法。与余弦和beta基线分类器相比，我们分别报告了49%和10%的改进。

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Natural language call routing: towards combination and boosting of classifiers

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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IEEE Workshop on Automatic Speech Recognition and Understanding, 2001. ASRU '01.

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