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        <title>REflex: Flexible Framework for Relation Extraction in Multiple Domains</title>
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        <namePart type="given">Geeticka</namePart>
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    <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 preprocessing 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.</abstract>
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%0 Conference Proceedings
%T REflex: Flexible Framework for Relation Extraction in Multiple Domains
%A Chauhan, Geeticka
%A McDermott, Matthew B.A.
%A Szolovits, Peter
%Y Demner-Fushman, Dina
%Y Cohen, Kevin Bretonnel
%Y Ananiadou, Sophia
%Y Tsujii, Junichi
%S Proceedings of the 18th BioNLP Workshop and Shared Task
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F chauhan-etal-2019-reflex
%X 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 preprocessing 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.
%R 10.18653/v1/W19-5004
%U https://aclanthology.org/W19-5004
%U https://doi.org/10.18653/v1/W19-5004
%P 30-47

Markdown (Informal)

[REflex: Flexible Framework for Relation Extraction in Multiple Domains](https://aclanthology.org/W19-5004) (Chauhan et al., BioNLP 2019)

• REflex: Flexible Framework for Relation Extraction in Multiple Domains (Chauhan et al., BioNLP 2019)

ACL

• Geeticka Chauhan, Matthew B.A. McDermott, and Peter Szolovits. 2019. REflex: Flexible Framework for Relation Extraction in Multiple Domains. In Proceedings of the 18th BioNLP Workshop and Shared Task, pages 30–47, Florence, Italy. Association for Computational Linguistics.