Leveraging Dependency Forest for Neural Medical Relation Extraction
This work addresses a domain-specific bottleneck in medical NLP by improving relation extraction accuracy for biomedical research.
The paper tackled the problem of low accuracy in medical relation extraction caused by 1-best dependency parse trees by using dependency forests and a graph neural network to filter noise, achieving state-of-the-art results on two biomedical benchmarks.
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.