MIT at SemEval-2017 task 10: relation extraction with convolutional neural networks
Name
1704.01523.pdf
Description
Accepted version
Size
213.08 KB
Format
Adobe PDF
Checksum (MD5)
d1048b3d415a4f182719a71247763a8a
Author(s)
Lee, Ji Young
Dernoncourt, Franck
Szolovits, Peter
Date Issued
2017
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
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
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.18653/v1/s17-2171
