Automatic Generation of Named Entity Distractors of Multiple Choice Questions Using Web Information

Abstract

This paper presents a novel technique for automatic generation of distractors for multiple choice questions. Distractors are the wrong choices given along with the correct answer (key) to befuddle the examinee. Various techniques have been proposed in the literature for automatic distractor generation. But none of these approaches are suitable when the key is a named entity. And named entity key or distractors are dominating in many domains including sports and entertainment. Here, we propose a technique for generation of named entity distractors. For generating good named entity distractors, we first detect the class of the key and collect a set of attribute values, classified into generic and specific categories. Based on these attributes, we retrieve a set of candidate distractors from a few trusted Web sites like Wikipedia. Then, we find the similarity between the key and a candidate distractor. The close ones are chosen as the final set of distractors. A set of human evaluators assess the distractors by using a set of parameters. In our evaluation, we observe that the system-generated distractors are good in terms of relevance and close to the key.