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Contextualized Latent Semantic Indexing: A New Approach to Automated Chinese Essay Scoring

机译:语境化潜在语义索引:自动汉语作文评分的新方法

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摘要

The writing part in Chinese language tests is badly in need of a mature automated essay scoring system. In this paper, we propose a new approach applied to automated Chinese essay scoring (ACES), called contextualized latent semantic indexing (CLSI), of which Genuine CLSI and Modified CLSI are two versions. The n-gram language model and the weighted finite-state transducer (WFST), two critical components, are used to extract context information in our ACES system. Not only does CLSI improve conventional latent semantic indexing (LSI), but bridges the gap between latent semantics and their context information, which is absent in LSI. Moreover, CLSI can score essays from the perspectives of language fluency and contents, and address the local overrating and underrating problems caused by LSI. Experimental results show that CLSI outperforms LSI, Regularized LSI, and latent Dirichlet allocation in many aspects, and thus, proves to be an effective approach.
机译:中文测试中的写作部分非常需要成熟的自动化论文评分系统。在本文中,我们提出了一种应用于自动中文论文评分(ACES)的新方法,称为上下文化潜在语义索引(CLSI),其真正的CLSI和修改的CLSI是两个版本。 N-GRAM语言模型和加权有限状态传感器(WFST),两个关键组件用于提取ACES系统中的上下文信息。 CLSI不仅改善了传统的潜在语义索引(LSI),而且弥合了LSI中缺席的潜伏语义与其上下文信息之间的差距。此外,CLSI可以从语言流畅和内容的角度来评分散文,并解决LSI引起的局部过度和低估问题。实验结果表明,CLSI优于LSI,正则化LSI和潜在的Dirichlet分配在许多方面,因此证明是一种有效的方法。

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