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Reliably detecting relevant relations between entities in unstructured textis a valuable resource for knowledge extraction, which is why it has awakensignificant interest in the field of Natural Language Processing. In thispaper, we present a system for relation classification and extraction based onan ensemble of convolutional and recurrent neural networks that ranked first in3 out of the 4 subtasks at SemEval 2018 Task 7. We provide detailedexplanations and grounds for the design choices behind the most relevantfeatures and analyze their importance.

ETH-DS3Lab at SemEval-2018 Task 7: Effectively Combining Recurrent and Convolutional Neural Networks for Relation Classification and Extraction