2020/05/31 by Abhinav Bhatt, Bhatt, Abhinav, Kaustubh Dhole +1
Biochemistry, Genetics and Molecular Biology · Computer Science · #Biomedical Text Mining and Ontologies #Computation and Language (cs.CL) #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Natural Language Processing Techniques #Topic Modeling
paper · pdf · doi:10.48550/arxiv.2006.00533
openalex publication_date 2020/05/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Extracting relationships and interactions between different biological entities is still an extremely challenging problem but has not received much attention as much as extraction in other generic domains. In addition to the lack of annotated data, low benchmarking is still a major reason for slow progress. In order to fill this gap, we compare multiple existing entity and relation extraction models over a recently introduced public dataset, BioRelEx of sentences annotated with biological entities and relations. Our straightforward benchmarking shows that span-based multi-task architectures like DYGIE show 4.9% and 6% absolute improvements in entity tagging and relation extraction respectively over the previous state-of-art and that incorporating domain-specific information like embeddings pre-trained over related domains boosts performance.