Relation Extraction in underexplored biomedical domains: A diversity-optimised sampling and synthetic data generation approach
The sparsity of labelled data is an obstacle to the development of Relation Extraction (RE)
models and the completion of databases in various biomedical areas. While being of high …
models and the completion of databases in various biomedical areas. While being of high …
BioBERT-based Deep Learning and Merged ChemProt-DrugProt for Enhanced Biomedical Relation Extraction
BT McInnes, J Tang, D Mahendran… - arxiv preprint arxiv …, 2024 - arxiv.org
This paper presents a methodology for enhancing relation extraction from biomedical texts,
focusing specifically on chemical-gene interactions. Leveraging the BioBERT model and a …
focusing specifically on chemical-gene interactions. Leveraging the BioBERT model and a …
[HTML][HTML] Investigating Cross-Domain Binary Relation Classification in Biomedical Natural Language Processing
This paper addresses the challenge of binary relation classification in biomedical Natural
Language Processing (NLP), focusing on diverse domains including gene-disease …
Language Processing (NLP), focusing on diverse domains including gene-disease …
Causal-Evidence Graph for Causal Relation Classification
This paper aims toward an enhancement for automatic causal relation classification from text
sources. We introduce a Causal Evidence Graph (CEG), which is a graph-structured …
sources. We introduce a Causal Evidence Graph (CEG), which is a graph-structured …
High-throughput Biomedical Relation Extraction for Semi-Structured Web Articles Empowered by Large Language Models
S Zhou, S Yu - arxiv preprint arxiv:2312.08274, 2023 - arxiv.org
Objective: To develop a high-throughput biomedical relation extraction system that takes
advantage of the large language models'(LLMs) reading comprehension ability and …
advantage of the large language models'(LLMs) reading comprehension ability and …