@InProceedings{suarezpaniagua-segurabedmar-martinez:2017:SemEval,
  author    = {Su\'{a}rez-Paniagua, V\'{i}ctor  and  Segura-Bedmar, Isabel  and  Mart\'{i}nez, Paloma},
  title     = {LABDA at SemEval-2017 Task 10: Relation Classification between keyphrases via Convolutional Neural Network},
  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     = {969--972},
  abstract  = {In this paper, we describe our participation at the subtask of extraction of
	relationships between two identified keyphrases. This task can be very helpful
	in improving search engines for scientific articles. Our approach is based on
	the use of a convolutional neural network (CNN) trained on the training
	dataset. This deep learning model has already achieved successful
	results for the extraction relationships between named entities. Thus, our
	hypothesis is that this model can be also applied to extract relations between
	keyphrases. The official results of the task show that
	our architecture obtained an F1-score of 0.38% for Keyphrases Relation
	Classification. This performance is lower than the expected due to the generic
	preprocessing phase and the basic configuration of the
	CNN model, more complex architectures are proposed as future work to increase
	the classification rate.},
  url       = {http://www.aclweb.org/anthology/S17-2169}
}