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A Topic-Independent Method for Scoring Student Essay Content

机译:一种与主题无关的评分学生论文内容的方法

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This paper proposes a topic-independent method for automatically scoring essay content. Unlike conventional topic-dependent methods, it predicts the human-assigned score of a given essay without training essays written to the same topic as the target essay. To achieve this, this paper introduces a new measure called MIDF that measures how important and relevant a word is in a given essay. The proposed method predicts the score relying on the distribution of MIDF. Surprisingly, experiments show that the proposed method achieves an accuracy of 0.848 and performs as well as or even better than conventional topic-dependent methods.
机译:本文提出了一种与主题无关的自动评分论文内容的方法。与传统的依赖主题的方法不同,它可以预测给定论文的人为评分,而无需培训与目标论文写在同一主题上的论文。为了实现这一目标,本文引入了一种称为MIDF的新度量,该度量可以度量单词在给定文章中的重要性和相关性。所提出的方法依靠MIDF的分布来预测得分。令人惊讶地,实验表明,所提出的方法达到了0.848的准确度,并且其性能优于或取决于传统的主题相关方法。

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