# Using Neural Networks for Relation Extraction from Biomedical Literature

## TL;DR

This paper explores neural network-based methods for extracting relations between biomedical concepts from literature, emphasizing the importance of data representation and biomedical ontologies to improve accuracy.

## Contribution

It demonstrates how multichannel neural architectures and the integration of biomedical ontologies enhance relation extraction performance.

## Key findings

- Multichannel neural networks achieve state-of-the-art results.
- Incorporating biomedical ontologies improves extraction accuracy.
- Optimal data representation combinations lead to higher evaluation scores.

## Abstract

Using different sources of information to support automated extracting of relations between biomedical concepts contributes to the development of our understanding of biological systems. The primary comprehensive source of these relations is biomedical literature. Several relation extraction approaches have been proposed to identify relations between concepts in biomedical literature, namely, using neural networks algorithms. The use of multichannel architectures composed of multiple data representations, as in deep neural networks, is leading to state-of-the-art results. The right combination of data representations can eventually lead us to even higher evaluation scores in relation extraction tasks. Thus, biomedical ontologies play a fundamental role by providing semantic and ancestry information about an entity. The incorporation of biomedical ontologies has already been proved to enhance previous state-of-the-art results.