MIT at SemEval-2017 Task 10: Relation Extraction with Convolutional Neural Networks

Ji Young Lee, Franck Dernoncourt, Peter Szolovits


Abstract
Over 50 million scholarly articles have been published: they constitute a unique repository of knowledge. In particular, one may infer from them relations between scientific concepts. Artificial neural networks have recently been explored for relation extraction. In this work, we continue this line of work and present a system based on a convolutional neural network to extract relations. Our model ranked first in the SemEval-2017 task 10 (ScienceIE) for relation extraction in scientific articles (subtask C).
Anthology ID:
S17-2171
Volume:
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
Month:
August
Year:
2017
Address:
Vancouver, Canada
Venue:
SemEval
SIGs:
SIGSEM | SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
978–984
Language:
URL:
https://aclanthology.org/S17-2171
DOI:
10.18653/v1/S17-2171
Bibkey:
Cite (ACL):
Ji Young Lee, Franck Dernoncourt, and Peter Szolovits. 2017. MIT at SemEval-2017 Task 10: Relation Extraction with Convolutional Neural Networks. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 978–984, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
MIT at SemEval-2017 Task 10: Relation Extraction with Convolutional Neural Networks (Lee et al., SemEval 2017)
Copy Citation:
PDF:
https://aclanthology.org/S17-2171.pdf