Applications Of Lexical Information For Algorithmically Composing Multiple-Choice Cloze Items

1 We report experience in applying techniques for natural language processing to algorithmically generating test items for both reading and listening cloze items. We propose a word sense disambiguationbased method for locating sentences in which designated words carry specific senses, and apply a collocation-based method for selecting distractors that are necessary for multiple-choice cloze items. Experimental results indicate that our system was able to produce a usable item for every 1.6 items it returned. We also attempt to measure distance between sounds of words by considering phonetic features of the words. With the help of voice synthesizers, we were able to assist the task of composing listening cloze items. By providing both reading and listening cloze items, we would like to offer a somewhat adaptive system for assisting Taiwanese children in learning English vocabulary.