Title: MIT at SemEval-2017 Task 10: Relation Extraction with Convolutional Neural Networks,
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, such as synonyms and hyponyms.
Artificial neural networks have been recently 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).