We propose a neural network model for joint extraction of named entities and relations between them, without any hand-crafted features. The key contribution of our model is to extend a BiLSTM-CRF-based entity recognition model with a deep biaffine attention layer to model second-order interactions between latent features for relation classification, specifically attending to the role of an entity in a directional relationship. On the benchmark "relation and entity recognition" dataset CoNLL04, experimental results show that our model outperforms previous models, producing new state-of-the-art performances.
@article{arxiv.1812.11275,
title = {End-to-end neural relation extraction using deep biaffine attention},
author = {Dat Quoc Nguyen and Karin Verspoor},
journal= {arXiv preprint arXiv:1812.11275},
year = {2019}
}
Comments
Proceedings of the 41st European Conference on Information Retrieval (ECIR 2019), to appear