Advancements in Communication and Systems
Named Entity Recognition on Biomedical Text
Authors: Priyanka Mishra, Anjan a and Anamika Larhgotra
Publishing Date: 12-02-2024
ISBN: 978-81-955020-7-3
Abstract
A crucial problem in NLP is named entity recognition (NER), which is used in biomedical text. It entails automatically locating and classifying particular items in scientific literature and medical records, including genes, proteins, illnesses, and medications. Due to the variety of terminology and context-dependent modifications, biomedical NER is difficult. Researchers use rule-based, deep learning, and machine learning techniques, as well as BERT model fine-tuning. Annotated datasets of the highest caliber, like the BioNLP Shared Tasks, are crucial. Accurate NER is essential for increasing clinical decision support, drug discovery, and medical research. It makes it possible to extract useful information from biomedical texts, advancing medical research and the study of life sciences.
Keywords
Biomedical text, named entity recognition, spaCy, NLP, challenges, BioBERT, BERT, BioNLP, Comparision.
Cite as
Priyanka Mishra, Anjan a and Anamika Larhgotra, "Named Entity Recognition on Biomedical Text", In: Ashish Kumar Tripathi and Vivek Shrivastava (eds), Advancements in Communication and Systems, SCRS, India, 2024, pp. 197-207. https://doi.org/10.56155/978-81-955020-7-3-18