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Community Question Answering (CAQ) systems play an important role in people's lives due to the huge knowledge accumulated in them. In order to take full advantage of the huge knowledge, the target of semeval2016 task3 is to find the best answers to a new question in CQA. This work proposes to use rich semantic text similarity (STS) features to complete the task. We address the task as a ranking problem and Support Vector Regression (SVR) model is chosen to combine rich semantic similarity features and context features. Finally, we used genetic algorithm to do feature selection. Our method achieves an MAP (mean average precision) of 71.52%, 71.43% and 48.49% in subtask A, B and C respectively. It ranked 8th in subtask A and subtask B, and 7th in subtask C.

ITNLP-AiKF at SemEval-2016 Task 3 a quesiton answering system using community QA repository