# Biomedical Flat and Nested Named Entity Recognition: Methods, Challenges, and Advances
## 5 Analysis of Entity Types and Span Representation
### Similarities between Different Entity Types  
Biomedical entities are often closely related across various types, and this characteristic is also reflected in word forms. These morphological similarities across different types may confuse the NER model. Therefore, this section observed the morphological similarities in practical datasets. For experiments, we integrated the seven widely available datasets: BioRED [43], JNLPBA [46], NCBI disease [65], BC2 GM [54], BC4 CHEMD [62], BC5 CDR [51], and Linnaeus [66]. All NEs within the datasets underwent a series of preprocessing steps, which included lemmatization, decapitalization, and the removal of special characters. Consequently, their types were normalized into eight classes: gene/protein, DNA, RNA, cell, disease, chemical, species, and variant. We defined the similarity of set \(A\) to \(B\) with respect to entity type as follows [97]:  
\[Similarity(A,\;B)=\frac{1}{|A|}\sum_{a\in A}\max_{b\in B}DSC(a,b), \tag{5}\]  
\[DSC(a,\;b)=\frac{2|a\cap b|}{|a|+|b|}. \tag{6}\]  
Here, \(a\) and \(b\) represent word collections of each entity. The similarity between word collections is calculated using the Dice-Sorensen coefficient (DSC). Note that this similarity measure is not symmetric for the entity types \(A\) and \(B\).  
Figure 4 illustrates the morphological textual similarities between biomedical entity types, revealing several noteworthy correlations. The strong correlations were observed among gene/protein, DNA, RNA, and cell entities. The pair of RNA and gene/protein exhibits the highest similarity score of 0.69. Pairs of DNA and gene/protein and RNA and DNA also showed high similarity with scores of 0.66 and 0.56, respectively. These outcomes are likely due to the functional and structural interconnectivity among them. For example, in'myeloid cells (cell)', the 'Mcl-1 (gene/protein)' is regulated by its promoter 'Mcl-1 promoter (DNA)', which controls the transcription of 'Mcl-1 mRNA (RNA)'. These morphological similarities can potentially increase the risk of model boundary misclassification when processing entities of that type simultaneously.  
Figure 4: Morphological textual similarity between biomedical entity types. Strong correlations were observed between gene/protein, DNA, RNA, and cell types exhibiting high biological similarity. The highest similarity is observed between RNA and gene/protein (0.69), followed by DNA and gene/protein (0.66). RNA and DNA also exhibit significant similarity (0.56). Cell correlates notably with gene/protein (0.5) and DNA (0.43).  
Another important consideration is that homonymous entities may be classified into different types depending on the dataset. For instance, 'angiotensin' is classified as a gene or gene product in BioRED and BC2 GM but as a chemical in BC4 CHEMD and BC5 CDR. Additional examples are provided in Supplementary Table S2. Recently, many studies have focused on integrating multiple datasets to develop multi-type NER models [74; 76]. However, our results reveal an inconsistency of type among the datasets, which inevitably hampers model performance. This discrepancy underscores the critical need for standardized annotation guidelines to ensure consistency across datasets. Additionally, post-processing techniques are necessary to maintain compatibility when integrating different existing datasets. Establishing these guidelines and addressing inconsistencies are essential for improving the accuracy and robustness of multi-type NER models.