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Investigating the importance of linguistic complexity features across different datasets related to language learning

机译:研究与语言学习相关的不同数据集的语言复杂性功能的重要性

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We present the results of our investigations aiming at identifying the most informative linguistic complexity features for classifying language learning levels in three different datasets. The datasets vary across two dimensions: the size of the instances (texts vs. sentences) and the language learning skill they involve (reading comprehension texts vs. texts written by learners themselves). We present a subset of the most predictive features for each dataset, taking into consideration significant differences in their per-class mean values and show that these subsets lead not only to simpler models, but also to an improved classification performance. Furthermore, we pinpoint fourteen central features that are good predictors regardless of the size of the linguistic unit analyzed or the skills involved, which include both morpho-syntactic and lexical dimensions.
机译:我们提出了旨在确定最具信息性的语言复杂性特征以对三个不同数据集中的语言学习水平进行分类的调查结果。数据集在两个维度上变化:实例的大小(文本与句子)以及它们所涉及的语言学习技能(阅读理解文本与学习者自己编写的文本)。考虑到每个类的均值之间的显着差异,我们为每个数据集提供了最具预测性的子集,并表明这些子集不仅可以简化模型,而且可以提高分类性能。此外,无论所分析的语言单元的大小或所涉及的技能如何,我们都精确确定了十四个中心特征,这些特征可以很好地预测目标,包括形态语法和词汇维度。

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