@InProceedings{vsaina-EtAl:2017:SemEval,
  author    = {\v{S}aina, Filip  and  Kukurin, Toni  and  Pulji\'{c}, Lukrecija  and  Karan, Mladen  and  \v{S}najder, Jan},
  title     = {TakeLab-QA at SemEval-2017 Task 3: Classification Experiments for Answer Retrieval in Community QA},
  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     = {339--343},
  abstract  = {In this paper we present the TakeLab-QA entry to SemEval 2017 task 3, which is
	a question-comment re-ranking problem. We present a classification based
	approach, including two supervised learning models -- Support Vector Machines
	(SVM) and Convolutional Neural Networks (CNN). We use features based on
	different semantic similarity models (e.g., Latent Dirichlet Allocation), as
	well as features based on several types of pre-trained word embeddings.
	Moreover, we also use some hand-crafted task-specific features. For training,
	our system uses no external labeled data apart from that provided by the
	organizers. Our primary submission achieves a MAP-score of 81.14 and F1-score
	of 66.99 -- ranking us 10th on the SemEval 2017 task 3, subtask A.},
  url       = {http://www.aclweb.org/anthology/S17-2055}
}