| COMPUTER SCIENCE AND TECHNOLOGY |
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Causal options in Chinese reading comprehension |
| WANG Yuanlong1, LI Ru1,2, ZHANG Hu1, WANG Zhiqiang1 |
1. School of Computer and Information Technology, Shanxi University, Taiyuan 030006, China;
2. Key Laboratory of Computation Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan 030006, China |
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Abstract A causal relation support analysis method based on a causal network is presented here to identify the causal relation types in Chinese reading comprehension. Firstly, the causal events are extracted from the literature by clue phrases, the causal relation between the events is given a value, and a causal network is constructed from the causal events and the causal relation. Then, the TF-IDF (term frequency-inverse document frequency) method is used to retrieve related sentences from the document and the importance of each word in the document to characterize the whole document. Finally, the causality network and related sentences are combined to analyze the causal support of the option. The method was evaluated using 769 articles and 13 Beijing colleges entrance examination (including the source text and the selected title) as a test set. This method then gave about 11% better result than the Baseline method.
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| Keywords
natural language processing
causality network
reading comprehension
semantic similarity
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Issue Date: 15 March 2018
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2018,
Vol. 58
