# Biomedical Flat and Nested Named Entity Recognition: Methods, Challenges, and Advances
###### Abstract  
Biomedical named entity recognition (BioNER) aims to identify and classify biomedical entities (i.e., diseases, chemicals, and genes) from text into predefined classes. This process serves as an important initial step in extracting biomedical information from textual sources. Considering the structure of the entities it addresses, BioNER tasks are divided into two categories: flat NER, where entities are non-overlapping, and nested NER, which identifies entities embedded within another. While early studies primarily addressed flat NER, recent advances in neural models have enabled more sophisticated approaches to nested NER, gaining increasing relevance in the biomedical field, where entity relationships are often complex and hierarchically structured. This review, thus, focuses on the latest progress in large-scale pre-trained language model-based approaches, which have shown the significantly improved performance of NER. The state-of-the-art flat NER models have achieved average F1-scores of 84% on BC2 GM, 89% on NCBI Disease, and 92% on BC4 CHEM, while nested NER models have reached 80% on the GENIA dataset, indicating room for enhancement. In addition, we discuss persistent challenges, including inconsistencies of named entities annotated across different corpora and the limited availability of named entities of various entity types, particularly for multi-type or nested NER. To the best of our knowledge, this paper is the first comprehensive review of pre-trained language model-based flat and nested BioNER models, providing a categorical analysis among the methods and related challenges for future research and development in the field. named entity recognition; biomedical named entity recognition; flat named entity recognition; nested named entity recognition; flat and nested named entity recognition; natural language processing +
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based on the structure of the target entities. The flat NER task recognizes entities that are a singular span of consecutive words without overlap or nesting with others. On the other hand, the nested NER recognizes all named entities (NEs) that either overlap or nest with others, considering the hierarchical properties of entities. The properties provide a more comprehensive understanding of themselves and their interrelationships. Biomedical NER (BioNER) poses significant challenges for several reasons [9; 10; 11]. One of the challenges is that a single term may encompass multiple types of entities depending on the context, introducing potential ambiguity. Moreover, biomedical terminologies are highly specialized and often differ from general ones, leading to suboptimal results when applied to embedding spaces designed for general corpora. Additionally, the continuous creation of new biomedical terms presents a challenge for BioNER to keep up with the latest vocabulary. In addition, biomedical NEs can cascade, introducing confusion about entity boundaries. For example, the DNA region \"HIV-2 enhancer\" contains the virus \"HIV-2\". Furthermore, biomedical entities often have descriptive names conveying important information about their functions, properties, or diagnostic criteria. Examples include \"clear-cell metastatic renal cell carcinoma\" and \"autosomal dominant nocturnal frontal lobe epilepsy\". These complications have highlighted the importance of nested NER to obtain more correct NEs. Early BioNER systems relied on predefined dictionaries or rules to identify NEs [12; 13], having limitations in recognizing NEs beyond the coverage of their predefined repertoires. Traditional machine learning (ML) approaches, on the other hand, learn patterns in features associated with NEs, such as capitalization, prefixes, suffixes, and part-of-speech tags, allowing them to identify entities with homologous meaning [14; 15; 16]. While these approaches can predict previously unseen NEs, they have the drawback of relying on manually designed features, which limits scalability and hinders the discovery of innovative patterns within the data.