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首页> 外文会议>IIAI International Congress on Advanced Applied Informatics >A Multi-model SVR Approach to Estimating the CEFR Proficiency Level of Grammar Item Features
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A Multi-model SVR Approach to Estimating the CEFR Proficiency Level of Grammar Item Features

机译：用于估计CEFR语法项目特征水平的多模型SVR方法

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Analysis of publicly available language learning corpora can be useful for extracting characteristic features of learners from different proficiency levels. This can then be used to support language learning research and the creation of educational resources. In this paper, we classify the words and parts of speech of transcripts from different speaking proficiency levels found in the NICT-JLE corpus. The characteristic features of learners who have the equivalent spoken proficiency of CEFR levels A1 through to B2 were extracted by analyzing the data with the support vector machine method. In particular, we apply feature selection to find a set of characteristic features that achieve optimal classification performance, which can be used to predict spoken learner proficiency.
机译：对公共语言学习语料库的分析对于从不同水平的学习者中提取学习者的特征非常有用。然后可以将其用于支持语言学习研究和创建教育资源。在本文中，我们对NICT-JLE语料库中不同语言水平的成绩单的单词和词性进行了分类。通过使用支持向量机方法分析数据，提取具有与CEFR从A1到B2相当的口语水平的学习者的特征。特别是，我们应用特征选择来找到一组可实现最佳分类性能的特征，这些特征可用于预测口语学习者的熟练程度。

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