UniMelb at SemEval-2016 task 3: Identifying similar questions by combining a CNN with string similarity measures

Abstract

This paper describes the results of the participation of The University of Melbourne in the community question-answering (CQA) task of SemEval 2016 (Task 3-B). We obtained a MAP score of 70.2% on the test set, by combining three classifiers: a NaiveBayes classifier and a support vector machine (SVM) each trained over lexical similarity features, and a convolutional neural network (CNN). The CNN uses word embeddings and machine translation evaluation scores as features.