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Automated Approaches for Detecting Integration in Student Essays

机译：检测学生散文集成的自动化方法

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Integrating information across multiple sources is an important literacy skill, yet there has been little research into automated methods for measuring integration in written text. This study investigated the efficacy of three different algorithms at classifying student essays according to an expert model of the essay topic which categorized statements by argument function, including claims and integration. A novel classification algorithm is presented which uses multi-word regular expressions. Its performance is compared to that of Latent Semantic Analysis and several variants of the Support Vector Machine algorithm at the same classification task. One variant of the SVM approach worked best overall, but another proved more successful at detecting integration within and across texts. This research has important implications for systems that can gauge the level of integration in written essays.
机译：整合跨多个来源的信息是一个重要的识字技能，但在书面文本中测量集成的自动化方法几乎没有研究。本研究根据论文主题的专家模型调查了三种不同算法在分类学生论文中的疗效，该专家模型由参数函数进行分类，包括索赔和集成。提出了一种使用多字正则表达式的新型分类算法。将其性能与潜在语义分析和支持向量机算法的若干变体进行比较。 SVM方法的一个变体总体上工作，但另一种证明在检测文本内部和跨文本内的集成方面更为成功。该研究对系统可以衡量书面论文中的整合程度的重要意义。

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