@inproceedings{khachatrian-etal-2019-biorelex,
title = "{B}io{R}el{E}x 1.0: Biological Relation Extraction Benchmark",
author = "Khachatrian, Hrant and
Nersisyan, Lilit and
Hambardzumyan, Karen and
Galstyan, Tigran and
Hakobyan, Anna and
Arakelyan, Arsen and
Rzhetsky, Andrey and
Galstyan, Aram",
editor = "Demner-Fushman, Dina and
Cohen, Kevin Bretonnel and
Ananiadou, Sophia and
Tsujii, Junichi",
booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-5019",
doi = "10.18653/v1/W19-5019",
pages = "176--190",
abstract = "Automatic extraction of relations and interactions between biological entities from scientific literature remains an extremely challenging problem in biomedical information extraction and natural language processing in general. One of the reasons for slow progress is the relative scarcity of standardized and publicly available benchmarks. In this paper we introduce BioRelEx, a new dataset of fully annotated sentences from biomedical literature that capture \textit{binding} interactions between proteins and/or biomolecules. To foster reproducible research on the interaction extraction task, we define a precise and transparent evaluation process, tools for error analysis and significance tests. Finally, we conduct extensive experiments to evaluate several baselines, including SciIE, a recently introduced neural multi-task architecture that has demonstrated state-of-the-art performance on several tasks.",
}
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<abstract>Automatic extraction of relations and interactions between biological entities from scientific literature remains an extremely challenging problem in biomedical information extraction and natural language processing in general. One of the reasons for slow progress is the relative scarcity of standardized and publicly available benchmarks. In this paper we introduce BioRelEx, a new dataset of fully annotated sentences from biomedical literature that capture binding interactions between proteins and/or biomolecules. To foster reproducible research on the interaction extraction task, we define a precise and transparent evaluation process, tools for error analysis and significance tests. Finally, we conduct extensive experiments to evaluate several baselines, including SciIE, a recently introduced neural multi-task architecture that has demonstrated state-of-the-art performance on several tasks.</abstract>
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%0 Conference Proceedings
%T BioRelEx 1.0: Biological Relation Extraction Benchmark
%A Khachatrian, Hrant
%A Nersisyan, Lilit
%A Hambardzumyan, Karen
%A Galstyan, Tigran
%A Hakobyan, Anna
%A Arakelyan, Arsen
%A Rzhetsky, Andrey
%A Galstyan, Aram
%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 khachatrian-etal-2019-biorelex
%X Automatic extraction of relations and interactions between biological entities from scientific literature remains an extremely challenging problem in biomedical information extraction and natural language processing in general. One of the reasons for slow progress is the relative scarcity of standardized and publicly available benchmarks. In this paper we introduce BioRelEx, a new dataset of fully annotated sentences from biomedical literature that capture binding interactions between proteins and/or biomolecules. To foster reproducible research on the interaction extraction task, we define a precise and transparent evaluation process, tools for error analysis and significance tests. Finally, we conduct extensive experiments to evaluate several baselines, including SciIE, a recently introduced neural multi-task architecture that has demonstrated state-of-the-art performance on several tasks.
%R 10.18653/v1/W19-5019
%U https://aclanthology.org/W19-5019
%U https://doi.org/10.18653/v1/W19-5019
%P 176-190
Markdown (Informal)
[BioRelEx 1.0: Biological Relation Extraction Benchmark](https://aclanthology.org/W19-5019) (Khachatrian et al., BioNLP 2019)
ACL
- Hrant Khachatrian, Lilit Nersisyan, Karen Hambardzumyan, Tigran Galstyan, Anna Hakobyan, Arsen Arakelyan, Andrey Rzhetsky, and Aram Galstyan. 2019. BioRelEx 1.0: Biological Relation Extraction Benchmark. In Proceedings of the 18th BioNLP Workshop and Shared Task, pages 176–190, Florence, Italy. Association for Computational Linguistics.