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://aclanthology.org/P19-1423
- DOI:
- 10.18653/v1/P19-1423
- Cite (ACL):
- Sunil Kumar Sahu, Fenia Christopoulou, Makoto Miwa, and Sophia Ananiadou. 2019. Inter-sentence Relation Extraction with Document-level Graph Convolutional Neural Network. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4309–4316, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Inter-sentence Relation Extraction with Document-level Graph Convolutional Neural Network (Sahu et al., ACL 2019)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/P19-1423.pdf