@InProceedings{attardi-EtAl:2017:SemEval,
  author    = {Attardi, Giuseppe  and  Carta, Antonio  and  Errica, Federico  and  Madotto, Andrea  and  Pannitto, Ludovica},
  title     = {FA3L at SemEval-2017 Task 3: A ThRee Embeddings Recurrent Neural Network for 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     = {299--304},
  abstract  = {In this paper we present ThReeNN, a model for Community Question Answering,
	Task 3, of SemEval-2017. The proposed model exploits both syntactic and
	semantic information to build a single and meaningful embedding space. Using a
	dependency parser in combination with word embeddings, the model creates
	sequences of inputs for a Recurrent Neural Network, which are then used for the
	ranking purposes of the Task. The score obtained on the official test data
	shows promising results.},
  url       = {http://www.aclweb.org/anthology/S17-2048}
}