TITLE: BioREx: Improving Biomedical Relation Extraction by Leveraging Heterogeneous Datasets 

ABSTRACT: Biomedical relation extraction (RE) is the task of automatically identifying
and characterizing relations between biomedical concepts from free text. RE is
a central task in biomedical natural language processing (NLP) research and
plays a critical role in many downstream applications, such as literature-based
discovery and knowledge graph construction. State-of-the-art methods were used
primarily to train machine learning models on individual RE datasets, such as
protein-protein interaction and chemical-induced disease relation. Manual
dataset annotation, however, is highly expensive and time-consuming, as it
requires domain knowledge. Existing RE datasets are usually domain-specific or
small, which limits the development of generalized and high-performing RE
models. In this work, we present a novel framework for systematically
addressing the data heterogeneity of individual datasets and combining them
into a large dataset. Based on the framework and dataset, we report on BioREx,
a data-centric approach for extracting relations. Our evaluation shows that
BioREx achieves significantly higher performance than the benchmark system
trained on the individual dataset, setting a new SOTA from 74.4% to 79.6% in
F-1 measure on the recently released BioRED corpus. We further demonstrate that
the combined dataset can improve performance for five different RE tasks. In
addition, we show that on average BioREx compares favorably to current
best-performing methods such as transfer learning and multi-task learning.
Finally, we demonstrate BioREx's robustness and generalizability in two
independent RE tasks not previously seen in training data: drug-drug N-ary
combination and document-level gene-disease RE. The integrated dataset and
optimized method have been packaged as a stand-alone tool available at
https://github.com/ncbi/BioREx.
Biomedical relation extraction (RE) is the task of automatically identifying
and characterizing relations between biomedical concepts from free text. RE is
a central task in biomedical natural language processing (NLP) research and
plays a critical role in many downstream applications, such as literature-based
discovery and knowledge graph construction. State-of-the-art methods were used
primarily to train machine learning models on individual RE datasets, such as
protein-protein interaction and chemical-induced disease relation. Manual
dataset annotation, however, is highly expensive and time-consuming, as it
requires domain knowledge. Existing RE datasets are usually domain-specific or
small, which limits the development of generalized and high-performing RE
models. In this work, we present a novel framework for systematically
addressing the data heterogeneity of individual datasets and combining them
into a large dataset. Based on the framework and dataset, we report on BioREx,
a data-centric approach for extracting relations. Our evaluation shows that
BioREx achieves significantly higher performance than the benchmark system
trained on the individual dataset, setting a new SOTA from 74.4% to 79.6% in
F-1 measure on the recently released BioRED corpus. We further demonstrate that
the combined dataset can improve performance for five different RE tasks. In
addition, we show that on average BioREx compares favorably to current
best-performing methods such as transfer learning and multi-task learning.
Finally, we demonstrate BioREx's robustness and generalizability in two
independent RE tasks not previously seen in training data: drug-drug N-ary
combination and document-level gene-disease RE. The integrated dataset and
optimized method have been packaged as a stand-alone tool available at
https://github.com/ncbi/BioREx.
BioREx: Improving Biomedical Relation Extraction by Leveraging Heterogeneous Datasets
http://arxiv.org/abs/2306.11189v1