Semantic relevancy between sentences for Chinese reading comprehension on college entrance examinations

GUO Shaoru; ZHANG Hu; QIAN Yili; LI Ru; YANG Zhizhuo; GU Zhaojun; MA Shuhui

doi:10.16511/j.cnki.qhdxxb.2017.26.021

PDF(1167 KB)

Journal of Tsinghua University(Science and Technology) ›› 2017, Vol. 57 ›› Issue (6) : 575-579,585. DOI: 10.16511/j.cnki.qhdxxb.2017.26.021

COMPUTER SCIENCE AND TECHNOLOGY

Semantic relevancy between sentences for Chinese reading comprehension on college entrance examinations

{{article.zuoZhe_EN}}

Author information +

History +

Abstract

Multiple-choice reading comprehension questions in the Chinese College Entrance Examination are based on the given background material with the reader selecting the best option from a number of options. The answer may not be directly found in the background material since the passage is relatively short and the key information is hidden. Thus, information mining from the background material and semantic relevancy analyses with options are keys to solving the problem, with sentence level semantic relevancy analysis as the foundation. This paper presents an algorithm to calculate the semantic relevancy between sentences based on Multi-Dimension Voting by analyzing large numbers of multiple-choice questions from Chinese scientific article text understanding questions from college entrance examinations. The method utilizes the voting algorithm to take advantage of different size metrics to select the best option. The algorithm accuracy for the national college entrance examination of Beijing text understanding questions is 53.84%, which verifies the validity of the method.

Key words

Chinese college entrance examination / text understanding / multiple-choice questions / multi-dimension voting / semantic relevancy

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

GUO Shaoru, ZHANG Hu, QIAN Yili, LI Ru, YANG Zhizhuo, GU Zhaojun, MA Shuhui. Semantic relevancy between sentences for Chinese reading comprehension on college entrance examinations[J]. Journal of Tsinghua University(Science and Technology). 2017, 57(6): 575-579,585 https://doi.org/10.16511/j.cnki.qhdxxb.2017.26.021

References

[1] 吴友政, 赵军, 段湘煜, 等. 问答式检索技术及评测研究综述[J]. 中文信息学报, 2005, 19(3):2-14. WU Youzheng, ZHAO Jun, DUAN Xiangyu, et al. Research on question answering & evaluation:A survey[J]. Journal of Chinese Information Processing, 2005, 19(3):2-14. (in Chinese) [2] Berant J, Chou A, Frostig R, et al. Semantic parsing on freebase from question-answer pairs[C]//Proceedings of EMNLP. Seattle, WA, USA:EMNLP, 2013:6-17. [3] Antoine Y B, Sumit C. Question answering with subgraph embeddings[C]//EMNLP. Doha, Qatar:EMNLP, 2014:615-620. [4] Ferrucci D, Brown E, Chu-Carroll J, et al. Building watson:An overview of the deep QA project[J]. AI Magazine, 2010, 31(3):59-79. [5] Zhang K, Wu W, Wang F, et al. Learning distributed representations of data in community question answering for question retrieval[C]//Ninth ACM International Conference on Web Search and Data Mining. Amsterdam, Holland:ACM Press, 2016:533-542. [6] 黄昌宁.从IBM深度问答系统战胜顶尖人类选手所想到的[J].中文信息学报, 2011, 25(6):21-25. HUANG Changning. Thinking about deep QA beating human champions[J]. Journal of Chinese Information Processing, 2011, 25(6):21-25. (in Chinese) [7] Richardson M, Burges C J C, Renshaw E. MCTest:A challenge dataset for the open-domain machine comprehension of text[C]//Proceedings of EMNLP. Seattle, WS, USA:EMNLP, 2013:193-203. [8] Narasimhan K, Barzilay R. Machine comprehension with discourse relations[C]//Meeting of the Association for Computational Linguistics and the International Joint Conference on Natural Language Processing. Beijing, China:ACL Press, 2015:1253-1262. [9] Sachan M, Dubey K, Xing E, et al. Learning answer-entailing structures for machine comprehension[C]//Meeting of the Association for Computational Linguistics and the, International Joint Conference on Natural Language Processing. Beijing, China:ACL Press, 2015:239-249. [10] Wang H, Bansal M, Gimpel K, et al. Machine comprehension with syntax, frames, and semantics[C]//Meeting of the Association for Computational Linguistics and the, International Joint Conference on Natural Language Processing. Beijing, China:ACL Press, 2015:700-706. [11] 刘群, 李素建. 基于《知网》的词汇语义相似度计算[J]. 中文计算语言学, 2002, 7(2):59-76.LIU Qun, LI Sujian. Word similarity computing based on how-net[J]. Computational Linguistics and Chinese Language Processing, 2002, 7(2):59-76. (in Chinese) [12] 郝晓燕, 刘伟, 李茹, 等. 汉语框架语义知识库及软件描述体系[J]. 中文信息学报, 2007, 21(5):96-100. HAO Xiaoyan, LIU Wei, LI Ru, et al. Description systems of the Chinese framenet database and software tools[J]. Journal of Chinese Information Processing, 2007, 21(5):96-100. (in Chinese) [13] Fillmore C J. Frame semantics and the nature of language[J]. Annals of the New York Academy of Sciences, 1976, 280(1):20-32. [14] Baker C F, Fillmore C J, Lowe J B. The Berkeley framenet project[C]//Annual Meeting of the Association for ComputationalLinguistics and 17th International Conference on Computational Linguistics-Volume 1. Montreal, Quebec, Canada:ACL Press, 1998:86-90. [15] Ruppenhofer J, Sporleder C, Morante R, et al. Semeval-2010 task 10:Linking events and their participants in discourse[C]//International Workshop on Semantic Evaluation. Uppsala, Sweden:ACL Press, 2010:45-50. [16] 李茹. 汉语句子框架语义结构分析技术研究[D]. 太原:山西大学, 2012. LI Ru. Research on Frame Semantic Structure Analysis Technology for Chinese Sentences[D]. Taiyuan:Shanxi University, 2012. (in Chinese) [17] Che W, Li Z, Liu T. LTP:A Chinese language technology platform[C]//International Conference on Computational Linguistics. Beijing, China:DBLP, 2010:13-16. [18] Dempster A P. Upper and lower probabilities induced by a multi-valued mapping[J]. Annals of Mathematical Statistics, 1967, 38(2):325-339. [19] Inglis J. A mathematical theory of evidence[J]. Technometrics, 1978, 20(1):242-242.