# Biomedical Flat and Nested Named Entity Recognition: Methods, Challenges, and Advances
## 3 Deep Learning Methods for Flat NER
### Machine Reading Comprehension-Based Approach  
MRC is an automated system designed to comprehend a passage and respond to questions. This approach has been applied in various NLP tasks. Figure 2b illustrates the process of an MRC-based approach designed for the NER task. This approach takes a sentence along with a query as input. The queries include information about the NEs,  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{3}{*}{\begin{tabular}{c} **Approach** \\ **Type** \\ \end{tabular} } & \multirow{3}{*}{**Model**} & \multicolumn{6}{c}{**Performance (F1-Score)**} \\ \cline{3-10}  & & **Gene/Protein** & **Disease** & \multicolumn{2}{c}{**Chemical**} & \multicolumn{2}{c}{**Species**} \\ \cline{3-10}  & & **BC2 GM** & **NCBI** & **Disease** & **BC5 DR** & **BC5 DR** & **BC4 CHEMD** & **LINNAEUS** & **Ref.** \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} Sequence \\ labeling \\ \end{tabular} } & BioBERT & 84.72 & 89.71 & 87.15 & 93.47 & 92.36 & 88.24 & Yes & [23] \\  & [PERSON] et al. & 86.05 & 91.23 & 88.34 & 94.24 & 92.28 & - & No & [24] \\ \hline \multirow{2}{*}{MRC} & BioBERT- & \multirow{2}{*}{85.48} & \multirow{2}{*}{90.04} & \multirow{2}{*}{87.83} & \multirow{2}{*}{94.19} & \multirow{2}{*}{92.92} & \multirow{2}{*}{-} & \multirow{2}{*}{Yes} & [72] \\  & MRC & & & & & & & & & \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Multi-task \\ learning \\ \end{tabular} } & MT-BioNER & 83.01 & 88.10 & - & 89.50 & - & - & No & [73] \\  & MTL-LS & 82.92 & 89.25 & 87.28 & 93.83 & 92.42 & 86.37 & No & [74] \\ \cline{1-1}  & BERT-CNN & 83.47 & 89.72 & - & - & 92.39 & 92.63 & Yes & [75] \\ \cline{1-1}  & AIONNER & - & 89.59 & 87.89 & 92.84 & - & 90.63 & Yes & [76] \\ \cline{1-1}  & TaughtNet & 84.84 & 89.20 & - & 93.95 & - & - & Yes & [77] \\ \hline \hline \end{tabular}
\end{table}
Table 2: A comprehensive list of methods for flat BioNER.  
such as entity type, definitions, and examples. Through the encoding layer, the sentence's embeddings incorporate the information from the query. Finally, in the boundary detection layer, the model identifies a set of spans corresponding to NEs.  
BioBERT-MRC, as proposed by [PERSON] et al., uses the MRC approach to address the flat NER problem [72]. Their input is defined as \"[CLS] _Sentence_ [SEP] _Query_ [SEP]\", where [CLS] and [SEP] are special tokens used to indicate the start of input and the separation between sentences, respectively. The query involves a request to detect NEs along with examples of such entities. The BioBERT is used for the encoding layer. In the boundary detection layer, two FFNNs are employed to predict the start and end indices of NEs. These two FFNNs operate sequentially, first predicting the start indices and then predicting the end indices with information on the start indices. Finally, the model generates final outputs by matching the nearest start and end indices. This model is limited in that it can only predict a single entity type per inference, even when using multi-type datasets. Moreover, the presence of queries may aggravate the inherent length limitations in PLM-based models. Nevertheless, the approach of incorporating external knowledge into the model through queries is interesting. The MRC system can also be utilized to solve nested or multi-type NEs.