SCESS: a WFSA-based automated simplified chinese essay scoring system with incremental latent semantic analysis
SCESS: a WFSA-based automated simplified chinese essay scoring system with incremental latent semantic analysis
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DOI：
10.1017/s1351324914000138
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发表时间：
2014-10
影响因子：
2.5
通讯作者：
Shudong Hao;Yanyan Xu;Dengfeng Ke;Kaile Su;Hengli Peng
中科院分区：
文献类型：
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作者：
Shudong Hao;Yanyan Xu;Dengfeng Ke;Kaile Su;Hengli Peng
摘要写作在语言测试中被认为是衡量考生语言能力的一项重要指标。随着汉语考试的普及，对考试组织者来说，给大量的作文打分成了一项繁重而昂贵的任务。在过去的几年里，已经做出了一些努力来开发自动化的简体中文作文评分系统，以减少成本和评估时间。本文介绍了一个基于加权有限状态自动机（WFSA）和增量潜在语义分析（ILSA）的中文作文评分系统SCESS（automated Simplified Chinese Essay Scoring System）。首先，SCESS使用n-gram语言模型来构造WFSA以执行文本预处理。在这一阶段，系统集成了混淆字符表，词性表，束搜索和启发式搜索，以执行自动分词和修改的文章。实验结果表明，该预处理过程是有效的，召回率为88.50%，检测精度为92.31%，纠正精度为88.46%。文本预处理后，SCESS使用ILSA进行自动作文评分。我们在少数民族汉语水平考试（MHK）的作文语料库上进行了实验，比较了ILSA方法和传统LSA方法。实验结果表明，ILSA具有显着的优势，LSA，在运行时间和内存使用方面。此外，实验结果也表明，SCESS是相当有效的评分性能为89.50%。
Abstract Writing in language tests is regarded as an important indicator for assessing language skills of test takers. As Chinese language tests become popular, scoring a large number of essays becomes a heavy and expensive task for the organizers of these tests. In the past several years, some efforts have been made to develop automated simplified Chinese essay scoring systems, reducing both costs and evaluation time. In this paper, we introduce a system called SCESS (automated Simplified Chinese Essay Scoring System) based on Weighted Finite State Automata (WFSA) and using Incremental Latent Semantic Analysis (ILSA) to deal with a large number of essays. First, SCESS uses an n-gram language model to construct a WFSA to perform text pre-processing. At this stage, the system integrates a Confusing-Character Table, a Part-Of-Speech Table, beam search and heuristic search to perform automated word segmentation and correction of essays. Experimental results show that this pre-processing procedure is effective, with a Recall Rate of 88.50%, a Detection Precision of 92.31% and a Correction Precision of 88.46%. After text pre-processing, SCESS uses ILSA to perform automated essay scoring. We have carried out experiments to compare the ILSA method with the traditional LSA method on the corpora of essays from the MHK test (the Chinese proficiency test for minorities). Experimental results indicate that ILSA has a significant advantage over LSA, in terms of both running time and memory usage. Furthermore, experimental results also show that SCESS is quite effective with a scoring performance of 89.50%.