| COMPUTER SCIENCE AND TECHNOLOGY |
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Semantic relevancy between sentences for Chinese reading comprehension on college entrance examinations |
| GUO Shaoru1, ZHANG Hu1, QIAN Yili1, LI Ru1,2, YANG Zhizhuo1, GU Zhaojun3, MA Shuhui1 |
1. School of Computer & Information Technology, Shanxi University, Taiyuan 030006, China;
2. Key Laboratory of Ministry of Education for Computation Intelligence & Chinese Information Processing, Shanxi University, Taiyuan 030006, China;
3. Information Security Evaluation Center, Civil Aviation University of China, Tianjin 300300, China |
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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.
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| Keywords
Chinese college entrance examination
text understanding
multiple-choice questions
multi-dimension voting
semantic relevancy
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Issue Date: 15 June 2017
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2017,
Vol. 57
