Unsupervised Relation Extraction 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). The approach 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. The paper examines three different surface pattern types, each implementing different assumptions about linguistic expression of semantic relations between named entities. Our main findings indicate that the approach is capable of achieving high precision rates 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 important semantic relations without any prior knowledge as to their types.