  We present the system we built for participating in SemEval-2016 Task 3 on
Community Question Answering. We achieved the best results on subtask C, and
strong results on subtasks A and B, by combining a rich set of various types of
features: semantic, lexical, metadata, and user-related. The most important
group turned out to be the metadata for the question and for the comment,
semantic vectors trained on QatarLiving data and similarities between the
question and the comment for subtasks A and C, and between the original and the
related question for Subtask B.
