Graph Convolution over Multiple Dependency Sub-graphs for Relation Extraction
Angrosh Mandya, Danushka Bollegala, Frans Coenen
Abstract
We propose a contextualised graph convolution network over multiple dependency-based sub-graphs for relation extraction. A novel method to construct multiple sub-graphs using words in shortest dependency path and words linked to entities in the dependency parse is proposed. Graph convolution operation is performed over the resulting multiple sub-graphs to obtain more informative features useful for relation extraction. Our experimental results show that the proposed method achieves superior performance over the existing GCN-based models achieving state-of-the-art performance on cross-sentence n-ary relation extraction dataset and SemEval 2010 Task 8 sentence-level relation extraction dataset. Our model also achieves a comparable performance to the SoTA on the TACRED dataset.
BibTeX
@inproceedings{mandya-etal-2020-graph,
title = "Graph Convolution over Multiple Dependency Sub-graphs for Relation Extraction",
author = "Mandya, Angrosh and
Bollegala, Danushka and
Coenen, Frans",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.565/",
doi = "10.18653/v1/2020.coling-main.565",
pages = "6424--6435"
}