# Biomedical Flat and Nested Named Entity Recognition: Methods, Challenges, and Advances
## 4 Deep Learning Methods for Nested NER
### Layer-Based Approaches  
The layer-based methods handle the hierarchy of nested entities by stacking multiple NER layers, designed to predict entities of a specific length or level (Figure 3a). The model progressively predicts entities by stacking layers, starting either from the layer that predicts the shortest (innermost) entities to the one that predicts the longest (outermost) entities, or vice versa. For instance, assume a model structured to predict entities from the innermost to the outermost levels. The NER layer 1 identifies entities that do not embed other entities within them. Following this, the NER layer 2 detects entities that embed those identified by the preceding layer. This process continues for subsequent layers, enabling the model to cover progressively broader spans of entities.  
[PERSON] and [PERSON] proposed Merge and Label, a layer-based model that identifies a set of entities by level [83]. The model determines the entity spans by assessing whether adjacent tokens and/or entities belong to the same entity, assigning a continuous real value to this prediction. The predicted real values are used as weights for the words and/or entities within the span, enabling a weighted sum calculation to derive the span embedding. These embeddings are then used to classify the spans. The model proceeds by passing tokens and/or spans to subsequent layers, thereby enabling the gradual formation of larger spans.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Approach Type**} & \multirow{2}{*}{**Method**} & \multicolumn{6}{c}{**Performance (F1-Score)**} & \multirow{2}{*}{**Code**} & \multirow{2}{*}{**Ref.**} \\ \cline{3-4} \multicolumn{1}{c}{} & & & **GENIA** & & & & & \\ \hline \multirow{5}{*}{Layer-based} & Merge Label & - \({}^{+}\) & - & - & 82.40 & - & Yes & [83] \\  & Pyramid & 79.19 & - & 86.28 & 84.66 & - & Yes & [84] \\ \hline \multirow{5}{*}{Encoder} & \multirow{2}{*}{Enumeration} & PURE & - & - & 88.10 & 88.70 & - & Yes & [85] \\  & & SpanMB & - & 81.80 & - & - & - & Yes & [86] \\  & & PL-Marker & - & - & 88.80 & 89.80 & - & Yes & [87] \\ \cline{2-8}  & \multirow{2}{*}{Boundary} & BENSC & 78.30 & - & 85.30 & 83.90 & - & No & [88] \\  & & Locate-and-Label & 80.54 & - & 87.41 & 86.67 & 84.05 & Yes & [89] \\ \cline{2-8}  & \multirow{2}{*}{MRC} & BERT-MRC & 83.75 & - & 85.98 & 86.88 & 80.97 & Yes & [90] \\  & & PIQN & 81.77 & - & 88.14 & 87.42 & 84.50 & Yes & [91] \\ \hline \multirow{5}{*}{Encoder} & \multirow{2}{*}{Sequence-to-set} & Sequence-to-Set & 80.44 & - & 87.26 & 87.05 & 83.96 & Yes & [92] \\  & & PnRNet & 81.85 & - & 88.12 & 87.63 & 85.27 & Yes & [93] \\ \cline{1-1} \cline{2-8}  & \multirow{2}{*}{Affine} & Biaffine & 80.50 & - & 86.70 & 85.40 & - & Yes & [94] \\ \cline{1-1}  & & Triaffine & 81.23 & - & 87.40 & 86.82 & - & Yes & [95] \\ \hline \hline \end{tabular}  
*—means that the model does not report performance for the given dataset.  
\end{table}
Table 3: A comprehensive list of methods for nested NER.  
[PERSON] et al. developed a layered model pyramid, which predicts complete entity mentions of the corresponding length in each layer [84]. The model consists of multiple interconnected layers and employs a convolutional network with two kernels between the layers to combine information from adjacent words and/or spans, forming a pyramid structure. Furthermore, the model operates in both forward and backward directions through an inverse pyramid, providing bidirectional interactions between adjacent layers. This model alleviates common issues of the general layer-based approaches, such as layer disorientation and error propagation. These studies demonstrate that the hierarchical structures of nested NEs can be handled in intuitive ways.