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A Feature-based Classification Technique for Answering Multi-choice World History Questions
May 05, 2015 Β· Declared Dead Β· π NTCIR Conference on Evaluation of Information Access Technologies
"No code URL or promise found in abstract"
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Authors
Shuangyong Song, Yao Meng, Zhongguang Zheng, Jun Sun
arXiv ID
1505.00863
Category
cs.IR: Information Retrieval
Cross-listed
cs.AI,
cs.CL
Citations
3
Venue
NTCIR Conference on Evaluation of Information Access Technologies
Last Checked
4 months ago
Abstract
Our FRDC_QA team participated in the QA-Lab English subtask of the NTCIR-11. In this paper, we describe our system for solving real-world university entrance exam questions, which are related to world history. Wikipedia is used as the main external resource for our system. Since problems with choosing right/wrong sentence from multiple sentence choices account for about two-thirds of the total, we individually design a classification based model for solving this type of questions. For other types of questions, we also design some simple methods.
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