@InProceedings{zhang-EtAl:2017:SemEval1,
  author    = {Zhang, Sheng  and  Cheng, Jiajun  and  Wang, Hui  and  Zhang, Xin  and  Li, Pei  and  Ding, Zhaoyun},
  title     = {FuRongWang at SemEval-2017 Task 3: Deep Neural Networks for Selecting Relevant Answers in Community Question Answering},
  booktitle = {Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {320--325},
  abstract  = {We describes deep neural networks frameworks in this paper to address the
	community question answering (cQA) ranking task (SemEval-2017 task 3).
	Convolutional neural networks and bi-directional long-short term memory
	networks are applied in our methods to extract semantic information from
	questions and answers (comments). In addition, in order to take the full
	advantage of question-comment semantic relevance, we deploy interaction layer
	and augmented features before calculating the similarity. The results show that
	our methods have the great effectiveness for both subtask A and subtask C.},
  url       = {http://www.aclweb.org/anthology/S17-2052}
}