The number of scientific publications in the literature is steadily growing, containing our
knowledge in the biomedical, health, and clinical sciences. Since there is currently no …

The biomedical literature is growing rapidly, and the extraction of meaningful information
from the large amount of literature is increasingly important. Biomedical named entity …

Abstract Motivation Large Language Models (LLMs) have the potential to revolutionize the
field of Natural Language Processing, excelling not only in text generation and reasoning …

Biomedical-named entity recognition (bNER) is critical in biomedical informatics. It identifies
biomedical entities with special meanings, such as people, places, and organizations, as …

Named entity recognition (NER) is an important step in biomedical information extraction
pipelines. Tools for NER should be easy to use, cover multiple entity types, be highly …

We present the approach of the Turku NLP group to the PharmaCoNER task on Spanish
biomedical named entity recognition. We apply a CRF-based baseline approach and …

The body of scientific literature continues to grow annually. Over 1.5 million abstracts of
biomedical publications were added to the PubMed database in 2021. Therefore …

Named Entity Recognition (NER) is a crucial natural language understanding task for many
down-stream tasks such as question answering and retrieval. Despite significant progress in …

Named entity recognition (NER) is a key component of many scientific literature mining
tasks, such as information retrieval, information extraction, and question answering; …

Motivation With the exponential growth of the life sciences literature, biomedical text mining
(BTM) has become an essential technology for accelerating the extraction of insights from …