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Question Understanding in Community-Based Question Answering Systems
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NETL
In this paper， we propose a novel method for community-based question answering task。 The proposed method takes advantage of the bidirectional long short-term memory to represent questions and answers in combination with an attention mechanism。 The attention model based on a multilayer perceptron captures important information in questions and their candidate sentences。 We conduct experiments on public datasets， published by SemEval workshop。 The experimental results show that our method achieves state-of-the-art performance。
NETL
