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.
@article{arxiv.1804.02042,
title = {ETH-DS3Lab at SemEval-2018 Task 7: Effectively Combining Recurrent and Convolutional Neural Networks for Relation Classification and Extraction},
author = {Jonathan Rotsztejn and Nora Hollenstein and Ce Zhang},
journal= {arXiv preprint arXiv:1804.02042},
year = {2018}
}
Comments
Accepted to SemEval 2018 (12th International Workshop on Semantic Evaluation)