An Empirical Study on Relation Extraction in the Biomedical Domain

TL;DR

This paper empirically evaluates relation extraction methods in biomedical texts, revealing their strong generalization but dependence on large labeled datasets, guiding future model development in the domain.

Contribution

It provides the first comprehensive empirical analysis of relation extraction techniques specifically in biomedical research articles.

Findings

Document-level methods generalize well across datasets.

Existing methods need extensive labeled data for fine-tuning.

Insights may inspire development of more effective biomedical relation extraction models.

Abstract

Relation extraction is a fundamental problem in natural language processing. Most existing models are defined for relation extraction in the general domain. However, their performance on specific domains (e.g., biomedicine) is yet unclear. To fill this gap, this paper carries out an empirical study on relation extraction in biomedical research articles. Specifically, we consider both sentence-level and document-level relation extraction, and run a few state-of-the-art methods on several benchmark datasets. Our results show that (1) current document-level relation extraction methods have strong generalization ability; (2) existing methods require a large amount of labeled data for model fine-tuning in biomedicine. Our observations may inspire people in this field to develop more effective models for biomedical relation extraction.