arXiv Machine Learning
BioBigBird: A Sparse Attention Model for Long-Range Dependency Processing in Biomedical Text
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arXiv AI
Sep 2
MiNER: Fine-Tuned Biomedical Natural Language Processing for Malaria Disease Entity Recognition in Clinical Texts
The paper introduces MiNER, a fine‑tuned biomedical NLP system that uses BioBERT to extract malaria‑related named entities from scientific literature. It builds a large, annotated corpus of malaria articles, preprocesses the text, and applies supervised learning to improve extraction performance. Experiments show that MiNER outperforms other encoding and machine‑learning methods in precision, recall, and accuracy, and the authors release the human‑labeled dataset for further research.
arXiv AI
Jun 16
Few-Shot Biomedical Relation Extraction with Large Language Models: A Viable Alternative to Supervised Learning?
arXiv:2606. 15412v1 Announce Type: cross Abstract: Biomedical relation extraction (BioRE) is a key step in transforming biomedical literature into structured knowledge.
arXiv Computation and Language
Sep 18
Fine-Tuning Models for Biomedical Relation Extraction
The paper introduces pre‑trained models for extracting variant‑phenotype relations from biomedical text, focusing on the SNPPhenA corpus. Fine‑tuning small BERT‑based models, especially DeBERTa, achieves performance close to the current state‑of‑the‑art. Moreover, careful fine‑tuning of Google’s Gemini Pro 1.0 surpasses existing benchmarks on both sentence‑level and abstract‑level relation extraction tasks.
arXiv AI
Jun 10
Attention Expansion: Enhancing Keyphrase Extraction from Long Documents with Attention-Augmented Contextualized Embeddings
arXiv:2606. 10716v1 Announce Type: cross Abstract: Pre-trained language models (PLMs) have achieved strong performance in keyphrase extraction (KPE), largely due to their ability to generate rich contextualized representations.
arXiv AI
Jul 13
TheBioCollection: Unified Pre-Training Scale LLM Corpus for Biology
arXiv:2607. 08803v1 Announce Type: cross Abstract: The push toward large language models for biology (BioLM) has created a need for training corpora that can endow models with a genuine understanding of biology.
arXiv Computation and Language
Sep 17
Benchmarking Large Language Models for Biomedical Relation Extraction
arXiv:2609.19071v1 Announce Type: new Abstract: Extracting SNP-phenotype associations from biomedical literature is vital but challenging. We benchmarked diverse NLP models, including MLMs, hybrid ar...