Biomedical NER with Pre-Trained Language Models (2025)

Named Entity Recognition (NER) in Biomedical Texts Using Pre-Trained Language Models | 2025 PROJECT

ABSTRACT
The field of Natural Language Processing (NLP)
has witnessed remarkable progress in recent years, particularly
in the domain of biomedical text analysis. Named Entity
Recognition (NER), a pivotal task in information extraction,
holds the key to deciphering and extracting structured
information from unstructured biomedical texts. Accurate
identification and classification of entities, such as DNA,
proteins, cell types, cell lines, and RNA, are imperative for
advancing our comprehension of complex biological systems.
This paper presents a comprehensive exploration of the
application of state-of-the-art pre-trained language models,
including Bert-base-cased, Distilbert-base-cased, Albert-baseV2, Xml-roberta-base, Ernie-2.0-base-en, and Conv-bert-base,
for structured Named Entity Recognition in biomedical texts.
The BioNLP2004 dataset, enriched with diverse entity types,
forms the basis for our experiments. Our objectives encompass
a thorough investigation into the effectiveness of different pretrained language models for biomedical NER, an in-depth
analysis of the challenges posed by the BioNLP2004 dataset, and
a comparative evaluation of the selected models in terms of
precision, recall, and F1 score. Additionally, we explore the
impact of fine-tuning strategies on model performance. The
insights gained from this research have the potential to advance
the capabilities of language models in the biomedical domain,
contributing to more efficient and precise biomedical text
analysis. This work serves as a stepping stone towards the
broader goals of bioinformatics and medical research. The
paper concludes with a summary of findings and outlines
potential avenues for future research.


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