A Feature-based Classification Technique for Answering Multi-choice World History Questions
Shuangyong Song, Yao Meng, Zhongguang Zheng, Jun Sun
TL;DR
This paper presents a feature-based classification system for answering multiple-choice world history questions, utilizing Wikipedia as an external resource, and introduces specialized models for different question types.
Contribution
The paper introduces a novel classification-based approach tailored for world history multiple-choice questions, with specific models for different question formats.
Findings
Effective classification model for multiple-choice questions
Utilization of Wikipedia as external knowledge source
Improved accuracy on real-world exam questions
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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Taxonomy
TopicsMachine Learning and Algorithms · Topic Modeling · Educational Technology and Assessment