UniMelb at SemEval-2016 Task 3: Identifying Similar Questions by combining a CNN with String Similarity Measures
Timothy J. Baldwin, Huizhi Liang, Bahar Salehi, Doris Hoogeveen, Yitong Li, Long Duong · 2016
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.