# REflex: Flexible Framework for Relation Extraction in Multiple Domains

arXiv: https://arxiv.org/abs/1906.08318 · HTML: https://mlsanity.com/paper/1906.08318/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jun 2019.

## Built on by (most cited first, 1 of 1)

- [Why only Micro-F1? Class Weighting of Measures for Relation Classification](https://mlsanity.com/paper/2205.09460/) (2022)

## Abstract (arXiv)

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.