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A discourse coherence model for analyzing Chinese students' essay

机译:分析中国学生文章的话语一致性模式

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Many attempts on improving entity grid model have been made and boost the research on text coherence. However, only a few of them applied it to noisy data domain and students' essay. Thus, we proposed a novel discourse coherence model, which is based on entity grid model, to evaluate coherence on Chinese students' essay. In allusion to the feature of Chinese students' essay, frequently using lexical repetition and coreference methods in coherence essay, we designed a coreference module, instead of clustering algorithm or knowledge base search methods, to integrate with the enhanced coherence model. In addition, we fully merged coreference feature into similarity assessment of adjacent sentences, and the semantic coherence. Experiments show that our model outperforms entity-based model and LSA methods and has an ideal effect on students' essay automatic assessment.
机译:已经制定了许多关于改进实体网格模型的尝试并提高了文本连贯性的研究。但是,只有少数人将其应用于嘈杂的数据域和学生的论文。因此,我们提出了一种新的话语一致性模型,基于实体网格模型,从而评估中国学生论文的一致性。在暗示中对中国学生的文章的特征,经常使用词法重复和Coreference方法在一致论文中,我们设计了一个Coreference模块,而不是聚类算法或知识库搜索方法,以与增强的相干模型集成。此外,我们完全将Coreference功能与相邻句子的相似性评估,以及语义连贯性。实验表明,我们的模型优于基于实体的模型和LSA方法,对学生的文章自动评估具有理想的影响。

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