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首页> 外文会议>International conference on web-based learning;International symposium on emerging technologies for education >Comparing the Performance of Latent Semantic Analysis and Probability Latent Semantic Analysis Models on Autoscoring Essay Tasks
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Comparing the Performance of Latent Semantic Analysis and Probability Latent Semantic Analysis Models on Autoscoring Essay Tasks

机译：自动评分论文任务的潜在语义分析和概率潜在语义分析模型的性能比较

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This paper evaluates the performance variances of Latent Semantic Analysis (LSA) and Probability Latent Semantic Analysis (PLSA) by judging essay text qualities as automated essay (AES) scoring tools. A correlation research design was used to examine the correlation between LSA performance and PLSA performance. We introduced 3 weight methods and performed 6 experiments to produce the scoring performances of both LSA and PLSA from a total of 2444 Chinese essays. The results show that there were strong correlations between the LSA scores and PLSA scores. While the overall performance of PLSA is better than that of LSA, the findings from the current study do not corroborate the previous findings for PLSA methods that claim a significant improvement. The implications of our research for AES reveal that both LSA and PLSA have a limited capability at this point and those more reliable measures for automated essay analyzing and scoring, such as text formats and forms, still need to be a component of text quality analysis.
机译：本文通过将论文文本质量作为自动论文（AES）评分工具，来评估潜在语义分析（LSA）和概率潜在语义分析（PLSA）的性能差异。相关研究设计用于检查LSA性能和PLSA性能之间的相关性。我们引入了3种权重方法，并进行了6次实验，从2444篇中文论文中得出LSA和PLSA的评分表现。结果表明，LSA分数和PLSA分数之间存在很强的相关性。尽管PLSA的总体性能优于LSA，但当前研究的结果并不能证实以前声称具有显着改善的PLSA方法的发现。我们对AES的研究表明，LSA和PLSA在这方面的能力有限，并且那些用于自动作文分析和评分的更可靠的措施，例如文本格式和形式，仍然需要作为文本质量分析的一部分。

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