Inter-sentence Relation Extraction with Document-level Graph Convolutional Neural Network

Sunil Kumar Sahu, Fenia Christopoulou, Makoto Miwa, Sophia Ananiadou


Abstract
Inter-sentence relation extraction deals with a number of complex semantic relationships in documents, which require local, non-local, syntactic and semantic dependencies. Existing methods do not fully exploit such dependencies. We present a novel inter-sentence relation extraction model that builds a labelled edge graph convolutional neural network model on a document-level graph. The graph is constructed using various inter- and intra-sentence dependencies to capture local and non-local dependency information. In order to predict the relation of an entity pair, we utilise multi-instance learning with bi-affine pairwise scoring. Experimental results show that our model achieves comparable performance to the state-of-the-art neural models on two biochemistry datasets. Our analysis shows that all the types in the graph are effective for inter-sentence relation extraction.
Anthology ID:
P19-1423
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4309–4316
Language:
URL:
https://www.aclweb.org/anthology/P19-1423
DOI:
10.18653/v1/P19-1423
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/P19-1423.pdf
Video:
 https://vimeo.com/385201042