# REflex: Flexible Framework for Relation Extraction in Multiple Domains

## TL;DR

REflex is a comprehensive framework for relation extraction across multiple domains, emphasizing the importance of pre-processing choices and providing insights and recommendations for future research in the field.

## Contribution

The paper introduces REflex, a unifying, extendable framework for relation extraction, and systematically explores factors affecting performance across diverse datasets.

## Key findings

- Pre-processing choices significantly impact RE performance.
- Omission of detailed methodology hampers fair comparison.
- Insights lead to recommendations for future research.

## Abstract

Systematic comparison of methods for relation extraction (RE) is difficult because many experiments in the field are not described precisely enough to be completely reproducible and many papers fail to report ablation studies that would highlight the relative contributions of their various combined techniques. In this work, we build a unifying framework for RE, applying this on three highly used datasets (from the general, biomedical and clinical domains) with the ability to be extendable to new datasets. By performing a systematic exploration of modeling, pre-processing and training methodologies, we find that choices of pre-processing are a large contributor performance and that omission of such information can further hinder fair comparison. Other insights from our exploration allow us to provide recommendations for future research in this area.