Leveraging Dependency Forest for Neural Medical Relation Extraction

TL;DR

This paper introduces a novel approach using dependency forests and graph neural networks to improve medical relation extraction from text, outperforming traditional tree-based methods and achieving state-of-the-art results.

Contribution

The paper proposes leveraging dependency forests with graph neural networks to enhance relation extraction accuracy in the medical domain, addressing parse tree inaccuracies.

Findings

Outperforms standard tree-based methods on biomedical benchmarks

Achieves state-of-the-art results in medical relation extraction

Utilizes dependency forests to improve recall and reduce noise

Abstract

Medical relation extraction discovers relations between entity mentions in text, such as research articles. For this task, dependency syntax has been recognized as a crucial source of features. Yet in the medical domain, 1-best parse trees suffer from relatively low accuracies, diminishing their usefulness. We investigate a method to alleviate this problem by utilizing dependency forests. Forests contain many possible decisions and therefore have higher recall but more noise compared with 1-best outputs. A graph neural network is used to represent the forests, automatically distinguishing the useful syntactic information from parsing noise. Results on two biomedical benchmarks show that our method outperforms the standard tree-based methods, giving the state-of-the-art results in the literature.