BioNestedNER: A Hybrid Language Model Approach for Recognizing Nested, Discontinuous, and Multi-Type Named Entities

Named Entity Recognition (NER) is essential in Natural Language Processing (NLP) for extracting pertinent information from unstructured data. Traditional NER approaches assume continuous and non-overlapping entities, which can be limiting in real-world scenarios. This research introduces BioNestedNER, a hybrid method for nested, discontinuous, and multi-type entity recognition, with a focus on clinical and biomedical domains. Our approach employs a language model (encoder-only Transformer-based model) using a machine reading comprehension strategy, treating NER as a question-answering-like task. A Conditional Random Field also addresses multi-label sequence labeling for handling nested entities as multi-type entities. Evaluation in Portuguese demonstrated state-of-the-art performance in micro F1-Scores across two clinical corpora. In NestedClinBr, featuring nested and discontinuous entities, our method achieved an F1-Score of 0.863, surpassing the second-place result by 2.1%. In SemClinBr, with multi-type entities, an F1-Score of 0.782 was achieved, surpassing the second-place result by 11.5%. This paper also presents a new clinical corpus in Brazilian Portuguese annotated with nested and discontinuous entities, offering a valuable resource for developing and evaluating models handling these complex entities. In conclusion, BioNestedNER presents an adaptable and effective NER solution for nested, discontinuous, and multi-type entities, with the potential to benefit various clinical applications.