KeLP at SemEval-2016 task 3: Learning semantic relations between questions and answers

This paper describes the KeLP system par- ticipating in the SemEval-2016 Community Question Answering (cQA) task. The chal- lenge tasks are modeled as binary classifi- cation problems: kernel-based classifiers are trained on the SemEval datasets and their scores are used to sort the instances and pro- duce the final ranking. All classifiers and kernels have been implemented within the Kernel-based Learning Platform called KeLP. Our primary submission ranked first in Sub- task A, third in Subtask B and second in Sub- task C. These ranks are based on MAP, which is the referring challenge system score. Our approach outperforms all the other systems with respect to all the other challenge metrics.