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This paper describes the system we submitted to the task 3 (Community Question Answering) in SemEval 2016, which contains three subtasks, i.e., Question-Comment Similarity (subtask A), Question-Question Similarity (subtask B), and Question-External Comment Similarity (subtask C). For subtask A, we employed three different methods to rank question-comment pair, i.e., supervised model using traditional features, Convolutional Neural Network and Long-Short Term Memory Network. For subtask B, we proposed two novel methods to improve semantic similarity estimation between question-question pair by integrating the rank information of questioncomment pair. For subtask C, we implemented a two-step strategy to select out the similar questions and filter the unrelated comments with respect to the original question.

ECNU at SemEval-2016 Task 3: Exploring Traditional Method and Deep Learning Method for Question Retrieval and Answer Ranking in Community Question Answering