@InProceedings{tymoshenko-bonadiman-moschitti:2017:EMNLP2017,
  author    = {Tymoshenko, Kateryna  and  Bonadiman, Daniele  and  Moschitti, Alessandro},
  title     = {Ranking Kernels for Structures and Embeddings: A Hybrid Preference and Classification Model},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {897--902},
  abstract  = {Recent work has shown that Tree Kernels (TKs) and Convolutional Neural Networks
	(CNNs) obtain the state of the art in answer sentence reranking. Additionally,
	their combination used in Support Vector Machines (SVMs) is promising as it can
	exploit both the syntactic patterns captured by TKs and the embeddings learned
	by CNNs. However, the embeddings are constructed according to a classification
	function, which is not directly exploitable in the preference ranking algorithm
	of SVMs. In this work, we propose a new hybrid approach combining preference
	ranking applied to TKs and pointwise ranking applied to CNNs. We show that our
	approach produces better results on two well-known and rather different
	datasets: WikiQA for answer sentence selection and SemEval cQA for comment
	selection in Community Question Answering.},
  url       = {https://www.aclweb.org/anthology/D17-1093}
}