RESIDE: Improving Distantly-Supervised Neural Relation Extraction using Side Information

Shikhar Vashishth, Rishabh Joshi, Sai Suman Prayaga, Chiranjib Bhattacharyya, Partha Talukdar


Abstract
Distantly-supervised Relation Extraction (RE) methods train an extractor by automatically aligning relation instances in a Knowledge Base (KB) with unstructured text. In addition to relation instances, KBs often contain other relevant side information, such as aliases of relations (e.g., founded and co-founded are aliases for the relation founderOfCompany). RE models usually ignore such readily available side information. In this paper, we propose RESIDE, a distantly-supervised neural relation extraction method which utilizes additional side information from KBs for improved relation extraction. It uses entity type and relation alias information for imposing soft constraints while predicting relations. RESIDE employs Graph Convolution Networks (GCN) to encode syntactic information from text and improves performance even when limited side information is available. Through extensive experiments on benchmark datasets, we demonstrate RESIDE’s effectiveness. We have made RESIDE’s source code available to encourage reproducible research.
Anthology ID:
D18-1157
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Editors:
Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1257–1266
Language:
URL:
https://aclanthology.org/D18-1157
DOI:
10.18653/v1/D18-1157
Bibkey:
Cite (ACL):
Shikhar Vashishth, Rishabh Joshi, Sai Suman Prayaga, Chiranjib Bhattacharyya, and Partha Talukdar. 2018. RESIDE: Improving Distantly-Supervised Neural Relation Extraction using Side Information. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1257–1266, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
RESIDE: Improving Distantly-Supervised Neural Relation Extraction using Side Information (Vashishth et al., EMNLP 2018)
Copy Citation:
PDF:
https://aclanthology.org/D18-1157.pdf
Attachment:
 D18-1157.Attachment.pdf
Video:
 https://aclanthology.org/D18-1157.mp4
Code
 malllabiisc/RESIDE
Data
FIGER