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

## TL;DR

This paper presents a convolutional neural network-based system that effectively extracts relations between scientific concepts from scholarly articles, achieving top performance in the SemEval-2017 ScienceIE task.

## Contribution

It introduces a CNN model specifically designed for relation extraction in scientific texts, outperforming previous approaches in a competitive benchmark.

## Key findings

- Ranked first in SemEval-2017 Task 10 for relation extraction
- Demonstrated effectiveness of CNNs in scientific relation extraction
- Achieved state-of-the-art results on the ScienceIE dataset

## 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).