A Feature-based Classification Technique for Answering Multi-choice World History Questions
Information Retrieval
2015-05-06 v1 Artificial Intelligence
Computation and Language
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.
Cite
@article{arxiv.1505.00863,
title = {A Feature-based Classification Technique for Answering Multi-choice World History Questions},
author = {Shuangyong Song and Yao Meng and Zhongguang Zheng and Jun Sun},
journal= {arXiv preprint arXiv:1505.00863},
year = {2015}
}
Comments
5 pages, no figure