In natural language processing, relation extraction seeks to rationally understand unstructured text. Here, we propose a novel SpanBERT-based graph convolutional network (DG-SpanBERT) that extracts semantic features from a raw sentence using the pre-trained language model SpanBERT and a graph convolutional network to pool latent features. Our DG-SpanBERT model inherits the advantage of SpanBERT on learning rich lexical features from large-scale corpus. It also has the ability to capture long-range relations between entities due to the usage of GCN on dependency tree. The experimental results show that our model outperforms other existing dependency-based and sequence-based models and achieves a state-of-the-art performance on the TACRED dataset.
@article{arxiv.2004.03636,
title = {Efficient long-distance relation extraction with DG-SpanBERT},
author = {Jun Chen and Robert Hoehndorf and Mohamed Elhoseiny and Xiangliang Zhang},
journal= {arXiv preprint arXiv:2004.03636},
year = {2021}
}