  Biomedical Named Entity Recognition (NER) is a fundamental task of Biomedical
Natural Language Processing for extracting relevant information from biomedical
texts, such as clinical records, scientific publications, and electronic health
records. The conventional approaches for biomedical NER mainly use traditional
machine learning techniques, such as Conditional Random Fields and Support
Vector Machines or deep learning-based models like Recurrent Neural Networks
and Convolutional Neural Networks. Recently, Transformer-based models,
including BERT, have been used in the domain of biomedical NER and have
demonstrated remarkable results. However, these models are often based on
word-level embeddings, limiting their ability to capture character-level
information, which is effective in biomedical NER due to the high variability
and complexity of biomedical texts. To address these limitations, this paper
proposes a hybrid approach that integrates the strengths of multiple models. In
this paper, we proposed an approach that leverages fine-tuned BERT to provide
contextualized word embeddings, a pre-trained multi-channel CNN for
character-level information capture, and following by a BiLSTM + CRF for
sequence labelling and modelling dependencies between the words in the text. In
addition, also we propose an enhanced labelling method as part of
pre-processing to enhance the identification of the entity's beginning word and
thus improve the identification of multi-word entities, a common challenge in
biomedical NER. By integrating these models and the pre-processing method, our
proposed model effectively captures both contextual information and detailed
character-level information. We evaluated our model on the benchmark i2b2/2010
dataset, achieving an F1-score of 90.11. These results illustrate the
proficiency of our proposed model in performing biomedical Named Entity
Recognition.
