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Discourse Element Identification in Student Essays based on Global and Local Cohesion

机译:基于全局和局部衔接的学生论文中的语篇元素识别

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We present a method of using cohesion to improve discourse element identification for sentences in student essays. New features for each sentence are derived by considering its relations to global and local cohesion, which are created by means of cohesive resources and subtopic coverage. In our experiments, we obtain significant improvements on identifying all discourse elements, especially of +5% F1 score on thesis and main idea. The analysis shows that global cohesion can better capture thesis statements.
机译:我们提出一种使用内聚力来改善学生论文中句子的语篇元素识别的方法。考虑到句子与全局和局部衔接的关系,可以得出每个句子的新功能,这些关系是通过衔接资源和子主题覆盖范围创建的。在我们的实验中,我们在识别所有话语元素方面取得了显着进步,尤其是论文和主要思想方面的分数+ 5%。分析表明,整体凝聚力可以​​更好地捕捉论文陈述。

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