  This paper presents a novel approach to automatic generation of adequate
distractors for a given question-answer pair (QAP) generated from a given
article to form an adequate multiple-choice question (MCQ). Our method is a
combination of part-of-speech tagging, named-entity tagging, semantic-role
labeling, regular expressions, domain knowledge bases, word embeddings, word
edit distance, WordNet, and other algorithms. We use the US SAT (Scholastic
Assessment Test) practice reading tests as a dataset to produce QAPs and
generate three distractors for each QAP to form an MCQ. We show that, via
experiments and evaluations by human judges, each MCQ has at least one adequate
distractor and 84\% of MCQs have three adequate distractors.
