%0 Generic %1 rotsztejn2018ethds3lab %A Rotsztejn, Jonathan %A Hollenstein, Nora %A Zhang, Ce %D 2018 %K 2018 classification cnn ecl eth extraction network neural nlp nn relation rnn semeval semeval18 %T ETH-DS3Lab at SemEval-2018 Task 7: Effectively Combining Recurrent and Convolutional Neural Networks for Relation Classification and Extraction %U http://arxiv.org/abs/1804.02042 %X Reliably detecting relevant relations between entities in unstructured text is a valuable resource for knowledge extraction, which is why it has awaken significant interest in the field of Natural Language Processing. In this paper, we present a system for relation classification and extraction based on an ensemble of convolutional and recurrent neural networks that ranked first in 3 out of the 4 subtasks at SemEval 2018 Task 7. We provide detailed explanations and grounds for the design choices behind the most relevant features and analyze their importance.

@misc{rotsztejn2018ethds3lab, abstract = {Reliably detecting relevant relations between entities in unstructured text is a valuable resource for knowledge extraction, which is why it has awaken significant interest in the field of Natural Language Processing. In this paper, we present a system for relation classification and extraction based on an ensemble of convolutional and recurrent neural networks that ranked first in 3 out of the 4 subtasks at SemEval 2018 Task 7. We provide detailed explanations and grounds for the design choices behind the most relevant features and analyze their importance.}, added-at = {2018-04-25T14:12:39.000+0200}, author = {Rotsztejn, Jonathan and Hollenstein, Nora and Zhang, Ce}, biburl = {https://www.bibsonomy.org/bibtex/2fea6a0fe18346ae85807bd2cd2ee3608/schwemmlein}, description = {ETH-DS3Lab at SemEval-2018 Task 7: Effectively Combining Recurrent and Convolutional Neural Networks for Relation Classification and Extraction}, interhash = {488e4b6acaf6eed8ae153b99fce2af42}, intrahash = {fea6a0fe18346ae85807bd2cd2ee3608}, keywords = {2018 classification cnn ecl eth extraction network neural nlp nn relation rnn semeval semeval18}, note = {cite arxiv:1804.02042Comment: Accepted to SemEval 2018 (12th International Workshop on Semantic Evaluation)}, timestamp = {2018-09-13T10:33:34.000+0200}, title = {ETH-DS3Lab at SemEval-2018 Task 7: Effectively Combining Recurrent and Convolutional Neural Networks for Relation Classification and Extraction}, url = {http://arxiv.org/abs/1804.02042}, year = 2018 }