SciREX: Scientific Relation Extraction

Abstract

The rapid growth of biomedical literature makes it increasingly difficult to identify and organize meaningful knowledge. This project addresses the problem by focusing on <strong>relation extraction (RE)</strong>, i.e., detecting and classifying semantic relationships between biomedical entities within scientific abstracts. <br/> Our objective is to evaluate and compare multiple paradigms for scientific relation extraction on the BioRED dataset, a manually annotated benchmark of PubMed abstracts with diverse entities and relation types. Specifically, we investigate three complementary approaches: a <strong>classification-based model</strong> using BioBERT, a <strong>question answering formulation</strong> QA4RE, and lastly, <strong>generative models</strong> SciFive and REBEL. <br/> The central research question guiding our study is: <em>Which modeling paradigm offers the most effective and generalizable solution for biomedical relation extraction under the constraints of the BioRED dataset?</em>