Nested Named Entity Recognition using Multilayer BERT-based Model

In natural language processing, named entity recognition (NER) is a crucial task involving finding and categorizing text entities. The biomedical domain presents substantial hurdles due to the complex structure of the language and the existence of nested entities. This paper introduces an innovative method for Nested NER by utilizing a multilayer bidirectional encoder representation transformer (BERT)-based model, notably employing pretrained PubMedBERT. Our proposed model is designed to manage nested entities’ complexities effectively. We combined the robust contextual embeddings from PubMedBERT with a multilayer tagging process. This approach allowed the model to precisely differentiate between overlapping items, a frequent occurrence in biomedical literature. To assess the effectiveness of our Multilayer NER Model (MultilayerNERModel), we conducted thorough experiments on the BioNNE English Dataset, a dataset for a shared task of BioASQ competition. The findings suggest that employing a multilayer approach enhances the model’s ability to identify nested entities, resulting in the thorough detection of entities in biomedical texts. It earned the highest overall performance in English oriented track, with an F1 score of 67.30% and a macro F1 score of 56.36%. These results demonstrate the significant impact of utilizing a multilayer approach in Nested NER tasks, especially in the biomedical domain. The use of UMLS dictionaries, along with the MultilayerNERModel, further enhances the model’s performance in biomedical entity recognition.