Enhancing biomedical named entity recognition with parallel boundary detection and category classification

Named entity recognition is a fundamental task in natural language processing. Recognizing entities in biomedical text, known as the BioNER, is particularly crucial for cutting-edge applications. However, BioNER poses greater challenges compared to traditional NER due to (1) nested structures and (2) category correlations inherent in biomedical entities. Recently, various BioNER models have been developed based on region classification or large language models. Despite being successful, these models still struggle to balance handling nested structures and capturing category knowledge.

We present a novel parallel BioNER model, BEAN, designed to address the unique properties of biomedical entities while achieving a reasonable balance between handling nested structures and incorporating category correlations. Extensive experiments on five public NER datasets, including four biomedical datasets, demonstrate that BEAN achieves state-of-the-art performance.

The proposed BEAN is elaborately designed to achieve two key objectives of the BioNER task: clearly detecting entity boundaries and correctly classifying entity categories. It is the first BioNER model to handle nested structures and category correlations in parallel. We exploit head, tail, and contextualized features to efficiently detect entity boundaries via a triaffine model. To the best of our knowledge, we are the first to introduce a multi-label classification model for the BioNER task to extract entity category information without boundary guidance.