Building a Relation Extraction Baseline for Gene-Disease Associations: A Reproducibility Study

Menotti, Laura

arXiv.org Artificial Intelligence 

Reproducibility is an important task in scientific research. It is crucial for researchers to compare newly developed systems with the state-of-the-art to assess whether they made a breakthrough. However previous works may not be immediately reproducible, for example due to the lack of source code. In this work we reproduce DEXTER, a system to automatically extract Gene-Disease Associations (GDAs) from biomedical abstracts.[1] The goal is to provide a benchmark for future works regarding Relation Extraction (RE), enabling researchers to test and compare their results.

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