首页> 外文会议>Autonomous and intelligent systems >Features' Weight Learning towards Improved Query Classification
【24h】

Features' Weight Learning towards Improved Query Classification

机译:功能的权重学习,以改善查询分类

获取原文
获取原文并翻译 | 示例

摘要

This paper is an attempt to enhance query classification in call routing applications. We have introduced a new method to learn weights from training data by means of regression model. In this work, we have tested our method with tf-idf weighting scheme, but the approach can be applied to any weighting scheme. Empirical evaluations with several classifiers including Support Vector Machines (SVM), Maximum Entropy, Naive Bayes, and K-Nearest Neighbor (KNN) show substantial improvement in both macro and micro Fl measure.
机译:本文旨在增强呼叫路由应用程序中的查询分类。我们介绍了一种通过回归模型从训练数据中学习权重的新方法。在这项工作中,我们已经使用tf-idf加权方案测试了我们的方法,但是该方法可以应用于任何加权方案。使用包括支持向量机(SVM),最大熵,朴素贝叶斯和K最近邻(KNN)在内的多个分类器进行的经验评估显示,宏观和微观Fl度量均得到了显着改善。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号