An exam robot for sentence completion in high school English tests

doi:10.3969/j.issn.0253-2778.2016.03.008

Abstract

Abstract: Addressed in this paper is the problem of sentence completion in Chinese national college or high school entrance English examinations in which the most appropriate word or phrase from a given set shoud be chosen to complete a sentence. Although a variety of methods have been developed to solve this problem in the literature, these approaches mainly focused on language modeling (LM) and latent semantic analysis (LSA) to the best of our knowledge. An exam robot prototype was built by extending the language modeling and latent semantic analysis methods to verb tense analysis and long distance phrase extraction. Specifically speaking, the syntactic, lexical and semantic features are extracted separately using by means of LM and LSA as well as verb tense analysis and phrase extraction two methods developed by the authors. These features are then fed into a learning to rank model to build the exam robot. The proposed approach outperforms LM and LSA models by 4.0 percentage points, achieving 78% accuracy on the question sets for senior entrance exams and 76% accuracy on the question sets for college entrance exams.

Key words: exam robot, sentence completion, language model, phrase extraction, tense analysis

CLC Number:

TP391

CHEN Zhigang, LIU Qingwen, LIN Wei, WANG Yang, CHEN XiaoPing. An exam robot for sentence completion in high school English tests[J]. Journal of University of Science and Technology of China, 2016, 46(3): 231-237.

References

［1］
ZWEIG G, BURGES C J C. The Microsoft research sentence completion challenge[R]. Technical Report MSR-TR-2011-129, Microsoft, 2011.
[2] ZWEIG G, PLATT J C, MEEK C, et al. Computational approaches to sentence completion[C]// Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics. Je ju Island, Korea: Association for Computational Linguistics, 2012, 1: 601-610.
[3] LANDAUER T, DUMAIS S T. Latent semantic analysis[J]. Annual Review of Information Science and Technology, 2004, 38(1): 188-230.
[4] GUBBINS J, VLACHOS A. Dependency language models for sentence completion[C]// Proceedings of the Conference on Empirical Methods in Natural Language Processing. Seattle, USA: ACL Press, 2013: 1405-1410.
[5] MIKOLOV T, KARAFIT M, BURGET L. Recurrent neural network based language model[C]// 11th Annual Conference of the International Speech Communication Association. Makuhari, Japan: ISCA Press, 2010: 1045-1048.
[6] MIKOLOV T. Statistical language models based on neural networks[R]. Presentation at Google, Mountain View, 2nd April 2012.
[7] MCCARTHY D, NAVIGLI R. The English lexical substitution task[J].Language Resources and Evaluation, 2009, 43(2): 139-159.
[8] RITTER A, Mausam, Etzioni O. "A latent dirichlet allocation method for selectional preferences[C]// Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, USA: ACL Press, 2010: 424-434.
[9] DE MARNEFFE M C, MACCARTNEY B, MANNING C D. Generating typed dependency parses from phrase structure parses[C]// Proceedings of the Language Resources Evaluation Conference. Stanford, USA: IEEE Press, 2006, 6: 449-454.
[10] MANNING C D, SURDEANU M, BAUER J, et al. The Stanford CoreNLP natural language processing toolkit[C]// Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics. Baltimore, USA: ACL Press, 2014: 55-60.

()
()

[1]	NIE Tengfei, YU Haisuo, DU Shaofu. Agriculture supply chain optimization based on supply and demand uncertainty with government subsidy policies [J]. Journal of University of Science and Technology of China, 2017, 47(3): 267-273.
[2]	HU Chao, ZHANG Juyong. Shape approximation via sparse optimization [J]. Journal of University of Science and Technology of China, 2017, 47(9): 713-720.
[3]	LI Yan, ZHOU Shizhe, DENG Jiansong. Face sorting and stripe texture mapping of triangle mesh based on spectral decomposition [J]. Journal of University of Science and Technology of China, 2017, 47(9): 721-728.
[4]	HUO Jiamian, XIE Jingui. Modelling and forecasting of call center arrival process [J]. Journal of University of Science and Technology of China, 2017, 47(9): 770-777.
[5]	ZHAN Yongkun, YANG Wenfei, ZHANG Tianzhu. Relation aware network for weakly-supervised temporal action localization [J]. Journal of University of Science and Technology of China, 2021, 51(10): 753-765.
[6]	Liang Qipeng, Zhu Qiaohui, Kang Yu, Zhao YunBo. Estimation based approximating control for wireless networked control systems [J]. Journal of University of Science and Technology of China, 2021, 51(4): 327-334.
[7]	Li Huicong, Fu Xiaoming∗ . Anisotropic surface meshing using locally isometric embedding [J]. Journal of University of Science and Technology of China, 2020, 50(12): 1460-1471.
[8]	YU Jun, LI Yajie, CHENG Lilei, LIAN Shun, TAN Chang, DING Decheng, Liu Qi. Research and practice of plagiarism detection in program code assignments by college students [J]. Journal of University of Science and Technology of China, 2020, 50(8): 1048-1057.
[9]	ZHANG Wenjing, BAN Zhijie. Citation network’s influence maximization algorithm based on global influence [J]. Journal of University of Science and Technology of China, 2020, 50(8): 1058-1063.
[10]	REN Min, XU Ling, WANG Feng, WU Chao. Campus encyclopedia platform based on knowledge recommendation [J]. Journal of University of Science and Technology of China, 2020, 50(8): 1072-1076.
[11]	SUN Lei, ZHANG Yining, XUE Yanfang, QIAO Lishan, ZHANG Limei. Adaptive functional connectivity network learning and application in brain disorders identification [J]. Journal of University of Science and Technology of China, 2020, 50(8): 1102-1109.
[12]	BAI Jiangbo, YANG Yang, ZHANG Wensheng. A simulation of the synthetic aperture radar image based on improved CycleGAN [J]. Journal of University of Science and Technology of China, 2020, 50(8): 1181-1186.
[13]	XIN Shouyu, ZHENG Ruirui, ZHOU Yu, LIU Wenpeng, HE Jianjun. A one-shot learning algorithm using support set information during training [J]. Journal of University of Science and Technology of China, 2020, 50(8): 1187-1192.
[14]	WANG Zhihua, KANG Hongmei. Using splines on triangular mesh to solve PDE with nonhomogeneous boundary [J]. Journal of University of Science and Technology of China, 2020, 50(7): 901-905.
[15]	GONG Lejun , ZHOU Shehai, CHENG Yifei, GAO Zhihong, LI Huakang. Identification of PBMC-related cells of single cell RNA sequence data [J]. Journal of University of Science and Technology of China, 2020, 50(7): 1013-1018.