Combining Recurrent and Convolutional Neural Networks for Relation Classification
Abstract
This paper investigates two different neural architectures for the task of relation classification: convolutional neural networks and recurrent neural networks. For both models, we demonstrate the effect of different architectural choices. We present a new context representation for convolutional neural networks for relation classification (extended middle context). Furthermore, we propose connectionist bi-directional recurrent neural networks and introduce ranking loss for their optimization. Finally, we show that combining convolutional and recurrent neural networks using a simple voting scheme is accurate enough to improve results. Our neural models achieve state-of-the-art results on the SemEval 2010 relation classification task.
Cite
@article{arxiv.1605.07333,
title = {Combining Recurrent and Convolutional Neural Networks for Relation Classification},
author = {Ngoc Thang Vu and Heike Adel and Pankaj Gupta and Hinrich Schütze},
journal= {arXiv preprint arXiv:1605.07333},
year = {2016}
}
Comments
NAACL 2016