Question type analysis for question-answering applications in education

In this paper, we present a question-answering (QA) system as a virtual tutor for students in the 5th and 6th grades. Students ask questions and the QA system gives answers to their questions based on a knowledge base. Teaching materials for history and geography are considered as a knowledge source. Because question log is not available in developing QA systems, multiple choice questions (MCQs) in the learning and testing materials are regarded as a training corpus to learn question types, answer types and keywords for retrieval, where an MCQ consists of a stem and a set of options. Options from the same MCQ are grouped into a cluster. Clusters with common elements are merged into a larger cluster. A cluster is labelled with a nominal element selected from the corresponding stems. We also mine question patterns from the stems for question type analysis in the QA system. Because the questions created by instructors in MCQs and the questions asked by students may be different, we develop a procedure to collect possible questions from students in the 6th grade. In the experiments, we first evaluate the question type classification systems using the MCQ corpus and the student corpus with 5-fold cross validation, respectively. Then we train question type classifiers with the complete MCQ corpus, and test them on the student corpus. The student's and the instructor's questions are compared and analyzed.