A Feature-based Classification Technique for Answering Multi-choice World History Questions
This work addresses a domain-specific challenge in educational AI for world history exams, but it is incremental as it builds on existing classification techniques without major innovations.
The authors tackled the problem of answering multi-choice world history questions from university entrance exams by using Wikipedia as an external resource, achieving participation in the QA-Lab English subtask of NTCIR-11 with a classification-based model for the most common question type and simple methods for others.
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.