• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Technical Gazette >A Machine Learning Classification Algorithm for Vocabulary Grading in Chinese Language Teaching
【24h】

A Machine Learning Classification Algorithm for Vocabulary Grading in Chinese Language Teaching

机译：汉语教学中词汇评分机器学习分类算法

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Vocabulary grading is of great importance in Chinese vocabulary teaching. This paper starts with an analysis of the lexical attributes that affect lexical complexity, followed by an explanation of the extraction of lexical attribute information combined with the constructed word-formation knowledge base, the construction of mapping functions corresponding to lexical attributes, and the quantitative representation of the attributes that form the basis for vocabulary grading. Based on this, a machine learning classification algorithm is creatively applied to the Chinese vocabulary grading problem. Using the comparative analysis of vocabulary grading models based on common machine learning classification algorithms, the importance measurement analysis of Chinese vocabulary attributes based on different feature selection methods is performed, and a vocabulary grading model is constructed based on the machine learning classification algorithm and feature importance selection of different feature selection algorithms. A comparison of the experimental results demonstrated that the classification model based on the support vector machine (SVM) algorithm and top six attribute groups by the importance of feature selection received the best effect. To improve vocabulary grading, a variety of feature selection algorithms were used to fuse the importance of lexical attributes on average. Then an experiment was conducted for vocabulary grading combined with the Bagging + SVM integration algorithm and top six attribute groups by the importance of feature selection. The experimental results demonstrated that the combination scheme achieved a better effect.
机译：词汇评分在中国词汇教学中具有重要意义。本文从影响词汇复杂性的词汇属性的分析开始，然后解释与构建的字地形成知识库相结合的词汇属性信息，对应于词汇属性的映射函数以及定量表示的映射函数的构造构成词汇量分级基础的属性。基于此，创新了一种机器学习分类算法对汉语词汇分级问题。利用基于公共机器学习分类算法的词汇量分级模型的比较分析，执行了基于不同特征选择方法的汉语词汇属性的重要性测量分析，基于机器学习分类算法构建了词汇量分级模型和特征重要性选择不同的特征选择算法。实验结果的比较表明，基于支持向量机（SVM）算法和前六个属性组的分类模型通过特征选择的重要性得到了最佳效果。为了提高词汇量度，各种特征选择算法用于融合平均词汇属性的重要性。然后通过特征选择的重要性，对词汇分级进行词汇分级，并通过特征选择的重要性，进行了对词汇分级和前六个属性组。实验结果表明，组合方案达到了更好的效果。

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