START Conference Manager    

Multimodal DBN for Predicting High-Quality Answers in cQA portals

Haifeng Hu, Bingquan Liu, Baoxun Wang, Ming Liu and Xiaolong Wang

The 51st Annual Meeting of the Association for Computational Linguistics - Short Papers (ACL Short Papers 2013)
Sofia, Bulgaria, August 4-9, 2013


In this paper, we address the problem for predicting cQA answer quality as a classification task. We propose a multimodal deep belief nets based approach that operates in two stages: First, the joint representation is learned by taking both textual and non-textual features into a deep learning network. Then, the joint representation learned by the network is used as input features for a linear classifier. Extensive experimental results conducted on two cQA datasets demonstrate the effectiveness of our proposed approach.

START Conference Manager (V2.61.0 - Rev. 2792M)