  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.
