@InProceedings{pratap-EtAl:2018:S18-1,
  author    = {Pratap, Bhanu  and  Shank, Daniel  and  Ositelu, Oladipo  and  Galbraith, Byron},
  title     = {Talla at SemEval-2018 Task 7: Hybrid Loss Optimization for Relation Classification using Convolutional Neural Networks},
  booktitle = {Proceedings of The 12th International Workshop on Semantic Evaluation},
  month     = {June},
  year      = {2018},
  address   = {New Orleans, Louisiana},
  publisher = {Association for Computational Linguistics},
  pages     = {863--867},
  abstract  = {This paper describes our approach to SemEval-2018 Task 7 -- given an entity-tagged text from the ACL Anthology corpus, identify and classify pairs of entities that have one of six possible semantic relationships. Our model consists of a convolutional neural network leveraging pre-trained word embeddings, unlabeled ACL-abstracts, and multiple window sizes to automatically learn useful features from entity-tagged sentences. We also experiment with a hybrid loss function, a combination of cross-entropy loss and ranking loss, to boost the separation in classification scores. Lastly, we include WordNet-based features to further improve the performance of our model. Our best model achieves an F1(macro) score of 74.2 and 84.8 on subtasks 1.1 and 1.2, respectively.},
  url       = {http://www.aclweb.org/anthology/S18-1139}
}