MIT at SemEval-2017 task 10: relation extraction with convolutional neural networks

Author(s)
Lee, Ji Young; Dernoncourt, Franck; Szolovits, Peter
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Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/
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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). ©2017
Date issued
2017
URI
https://hdl.handle.net/1721.1/124431
Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Journal
Proceedings of the 11th International Workshop on Semantic Evaluation (Sem-Eval 2017)
Publisher
Association for Computational Linguistics
Citation
Lee, Ji Young, Franck Dernoncourt, and Peter Szolovits, "MIT at SemEval-2017 task 10: relation extraction with convolutional neural networks." Proceedings of the 11th International Workshop on Semantic Evaluations (SemEval-2017), August 3-4, 2017, Vancouver, Canada (Stroudsburg, PA: Association for Computational Linguistics, 2017): p. 978-84 doi http://dx.doi.org/10.18653/v1/s17-2171 ©2017 Author(s)
Version: Original manuscript
ISBN
978-1-945626-55-5
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