Automated Assessment of Subjective Tests

JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM ›› 2016, Vol. 18 ›› Issue (4): 108-116.

School of Automation, Beijing University of Posts and Telecommunications, Beijing 100876, China

Received:2016-03-24 Online:2016-08-31

Abstract

Abstract:

As one of the key technologies of smart education, automated assessment of subjective tests (AAST) is a crossed research direction on linguistics and natural language processing (NLP) and related disciplines Subjective questions will be divided into two types which have reference answers and open answers, and then based on the ways computing semantic similarity, the subjective questions with reference answers will be divided into three types which are simple word shape matching, use the universal knowledge and based on large-scale corpus The advances in evaluation technology and program on the subjective questions in domestic and overseas are introduced Then the advantages and disadvantages of each way are summarized and research point of deep learning can be learned to provide reference and inspiration for AAST on Chinese

Key words: computer automatic correcting, subjective tests, automated assessment, natural language processing, smart education

CLC Number:

LIU Wei, QI Zi-sen, WANG Mu-xuan. Automated Assessment of Subjective Tests[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2016, 18(4): 108-116.

References

［1］ Burstein J, Leacock C, Swartz R, et al. Automated evaluation of essays and short answers ［C］//Fifth International Computer Assisted Assessment Conference. Leicester: Loughborough University, 2001.
［2］   Burstein J, Wolff S, Lu C. Using lexical semantic techniques to classify free-responses［C］//Proceedings of SIGLEX 1996 Workshop, Annual Meeting of the Association of Computational Linguistics. Santa Cruz: University of California, 1996: 227-246.
［3］ Callear D, Jerrams-Smith J, Soh V. CAA of short Non-MCQ answers［C］//Fifth International Computer Assisted Assessment Conference. Leicester: Loughborough University, 2001.
［4］ Landauer T K, Foltz P W, Laham D. Introduction to latent semantic analysis ［J］. Discourse Processes, 1998, 25(3): 259-284.
［5］宋继光, 王强军, 杨尔弘. 中文信息处理教程［M］. 北京: 高等教育出版社, 2011: 9-10.
［6］任杰, 张晋军. 汉语测试电子评分员研究报告［J］. 中国考试, 2004(10): 27-32.
［7］曹亦薇, 杨晨. 使用潜语义分析的汉语作文自动评分研究［J］. 考试研究, 2007, 3（1）: 63-71.
［8］高思丹, 袁春凤. 语句相似度计算在主观题自动批改技术中的初步应用［J］. 计算机工程与应用, 2004, 40(14): 132-135.
［9］马昌威. 基于语义脉络的主观题自动评分算法探讨［J］. 淮海工业学院学报（自然科学版）, 2013, 22(1): 26-29
［10］曹玉婵, 左映龙. 浅析在线考试主观题智能阅卷算法的设计与实现［J］. 价值工程, 2014(17): 231-233.
［11］梁晓诚. 基于自然语言处理的主观题评分算法研究［J］. 昆明理工大学学报(理工版), 2010, 35(2): 81-84.
［12］姜振凤, 刘力. 基于计算机辅助评价的主观题自动测评研究［J］. 重庆师范大学学报（自然科学版）, 2013, 30(2): 74-78.
［13］赵白玉. 基于汉语依存句法分析的主观题自动评分研究［D］. 长沙: 湖南大学, 2012.
［14］丁振国, 陈海霞. 一种基于知网的主观题阅卷算法［J］. 微电子学与计算机, 2008, 25(5): 108-109.
［15］   张添一. 基于文本相似度计算的主观题自动阅卷技术研究［D］. 长春: 东北师范大学, 2011.
［16］刘群, 李素建. 基于知网的词汇语义相似度的计算［D］. 北京: 中国科学院计算技术研究所, 2002.
［17］   金博, 史彦军, 滕弘飞. 基于语义理解的文本相似度算法［J］. 大连理工大学学报, 2005, 45(2): 291-297.
［18］韩永国, 赵志宏. 基于NLP-TF技术的主观题自动测评系统研究［J］. 软件导刊, 2013, 12(1): 82-83.
［19］李玉红, 柴林燕, 张琪. 结合分词技术与语句相似度的主观题自动判分算法［J］. 计算机工程与设计, 2010, 31(11): 2663-2666.
［20］   田甜, 张振国. 主观题自动阅卷技术研究［J］. 计算机工程与设计, 2010, 31(16): 3697-3699.
［21］佟振宇. 论文主观题自动阅卷系统的研究与实现［D］. 沈阳: 沈阳工业大学, 2009.
［22］   李翠霞, 谭营军, 孔金生. 基于马氏距离的文本聚类算法在自动阅卷系统中的应用［J］. 计算机应用与软件, 2015(4): 80-82.
［23］   Mihalcea R. Graph-based ranking algorithms for sentence extraction, applied to text summarization［C］//the ACL 2004 on Interactive Poster and Demonstration Sessions. Barcelona, Spain, 2004.
［24］ Bengio Y, Ducharme R,   Vincent P. A neural probabilistic language model［C］ // Advances in Neural Information Processing Systems, 2001: 932-938.
［25］ Mikolov T,   Karafiát M,   Burget L, et al. Recurrent neural network based language model［C］// INTERSPEECH 2010, 11th Annual Conference of the International Speech Communication Association, 2010: 1045-1048.
［26］ Mikolov T, Kai C, Corrado G, et al. Efficient estimation of word representations in vector space［C］// International Conference on Learning Representations Workshop Track, 2013.
［27］ Quoc Le, Mikolov T. Distributed representations of sentences and documents［C］// Proceedings of the 31st International Conference on Machine Learning, Beijing, China, 2014.
［28］来斯惟. 基于神经网络的词和文档语义向量表示方法研究［D］. 北京: 中国科学院自动化研究所, 2016.