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dc.contributor.authorLee, Ji Young
dc.contributor.authorDernoncourt, Franck
dc.contributor.authorSzolovits, Peter
dc.date.accessioned2020-03-30T19:41:49Z
dc.date.available2020-03-30T19:41:49Z
dc.date.issued2017
dc.identifier.isbn978-1-945626-55-5
dc.identifier.urihttps://hdl.handle.net/1721.1/124431
dc.description.abstractOver 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). ©2017en_US
dc.language.isoen
dc.publisherAssociation for Computational Linguisticsen_US
dc.relation.isversionofhttp://dx.doi.org/10.18653/v1/s17-2171en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourcearXiven_US
dc.titleMIT at SemEval-2017 task 10: relation extraction with convolutional neural networksen_US
dc.typeArticleen_US
dc.identifier.citationLee, 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)en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.relation.journalProceedings of the 11th International Workshop on Semantic Evaluation (Sem-Eval 2017)en_US
dc.eprint.versionOriginal manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2019-07-10T17:16:35Z
dspace.date.submission2019-07-10T17:16:36Z
mit.metadata.statusComplete


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