We investigated how to automatically solve verbal comprehension questions in IQ Tests by using word embedding techniques.
we proposed a three-step framework: (i) to recognize the specific type of a verbal comprehension question by a classifier, (ii) to leverage a novel deep learning model to co-learn the representations of both wordsense pairs and relations among words (or their senses), (iii) to design dedicated solvers, based on the obtained word-sense pair representations and relation representations, for addressing each type of questions.