Multimodal DBN for Predicting High-Quality Answers in cQA portals

@inproceedings{Hu2013MultimodalDF,
  title={Multimodal DBN for Predicting High-Quality Answers in cQA portals},
  author={Haifeng Hu and Bingquan Liu and Baoxun Wang and Ming Liu and Xiaolong Wang},
  booktitle={ACL},
  year={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… CONTINUE READING
Highly Cited
This paper has 26 citations. REVIEW CITATIONS

Citations

Publications citing this paper.
Showing 1-10 of 16 extracted citations

Multimodal representation learning with neural networks

John Edilson Arevalo Ovalle
2018
View 1 Excerpt

Temporal Interaction and Causal Influence in Community-Based Question Answering

IEEE Transactions on Knowledge and Data Engineering • 2017
View 2 Excerpts

References

Publications referenced by this paper.

Similar Papers

Loading similar papers…