Unsupervised Relation Extraction Using Dependency Trees for Automatic Generation of Multiple-Choice Questions

In this paper, we investigate an unsupervised approach to Relation Extraction to be applied in the context of automatic generation of multiple-choice questions (MCQs). MCQs are a popular large-scale assessment tool making it much easier for test-takers to take tests and for examiners to interpret their results. Our approach to the problem aims to identify the most important semantic relations in a document without assigning explicit labels to them in order to ensure broad coverage, unrestricted to predefined types of relations. In this paper, we present an approach to learn semantic relations between named entities by employing a dependency tree model. Our findings indicate that the presented approach is capable of achieving high precision rates, which are much more important than recall in automatic generation of MCQs, and its enhancement with linguistic knowledge helps to produce significantly better patterns. The intended application for the method is an e-Learning system for automatic assessment of students’ comprehension of training texts; however it can also be applied to other NLP scenarios, where it is necessary to recognise the most important semantic relations without any prior knowledge as to their types.