# BERT-Based Transfer-Learning Approach for Nested Named-Entity Recognition Using Joint Labeling
## 7 Results and Discussion
### Comparison of the Results with Other Existing Approaches
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Source** & **Used Approach** & **F1-Score** \\ \hline
[21] &
\begin{tabular}{c} Parser-based \\ \end{tabular} & 70.33 \\ \hline
[23] &
\begin{tabular}{c} Mention-hypergraph-based \\ \end{tabular} & 68.70 \\ \hline
[25] &
\begin{tabular}{c} Multigraph-based \\ \end{tabular} & 70.80 \\ \hline
[51] &
\begin{tabular}{c} Neural-network-based (LSTM, hypergraph features) \\ \end{tabular} & 73.80 \\ \hline
[52] &
\begin{tabular}{c} Neural-network-based (LSTM-CRF, seq2 seq, \\ contextual embeddings) \\ \end{tabular} & 73.90 \\ \hline
[13] & \
\begin{tabular}{c} Neural-network-based (boundary aware Bi-LSTM) \\ \end{tabular} & 73.90 \\ \hline This Paper &
\begin{tabular}{c} Transfer-learning-based (best BERT model) \\ \end{tabular} & 74.38 \\ \hline
[16] &
\begin{tabular}{c} Neural-network-based (Bi-LSTM-CRF, \\ contextual embeddings) \\ \end{tabular} & 77.36 \\ \hline
[14] &
\begin{tabular}{c} Neural-network-based (seq2 seq, \\ contextual embeddings) \\ \end{tabular} & 78.31 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of results with existing approaches for GENIA dataset. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Source** & **Used Approach** & **F1-Score** \\ \hline
[13] &
\begin{tabular}{c} Neural-network-based (boundary aware Bi-LSTM) \\ \end{tabular} & 71.7 \\ \hline
[53] &
\begin{tabular}{c} Neural-network-based (feed forward, Bi-LSTM, \\ Win-bi-LSTM) \\ \end{tabular} & 76.12 \\ \hline
[54] &
\begin{tabular}{c} Neural-network-based (Bi-LSTM-CRF) \\ \end{tabular} & 75.3 \\ \hline
[17] & \
\begin{tabular}{c} Neural-network-based (head–tail pair, token \\ interaction tagger) \\ \end{tabular} & 72.6 \\ \hline This Paper &
\begin{tabular}{c} Transfer-learning-based (best BERT model) \\ \end{tabular} & 85.29 \\ \hline
[55] &
\begin{tabular}{c} Neural-network-based (PolDeepNer2) \\ \end{tabular} & 87.69 \\ \hline
[56] &
\begin{tabular}{c} Transfer-learning-based (unsupervised pretraining, \\ pretrained BERT) \\ \end{tabular} & 88.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of results with existing approaches for GermEval 2014 dataset. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Source** & **Used Approach** & **F1-Score** \\ \hline
[57] &
\begin{tabular}{c} Neural-network-based (Bi-LSTM, embeddings) \\ \end{tabular} & 78.4 \\ \hline
[17] &
\begin{tabular}{c} Neural-network-based (head–tail pair, token \\ interaction tagger) \\ \end{tabular} & 74.9 \\ \hline
[58] & \
\begin{tabular}{c} Neural-network-based (Bi-LSTM, embeddings) \\ \end{tabular} & 75.87 \\ \hline This Paper &
\begin{tabular}{c} Transfer-learning-based (best BERT model) \\ \end{tabular} & 80.48 \\ \hline
[59] &
\begin{tabular}{c} Neural-network-based (BLSTM-CNN-Char and \\ Spark NLP) \\ \end{tabular} & 81.29 \\ \hline
[60] &
\begin{tabular}{c} Transformer-based \\ \end{tabular} & 82.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of results with existing approaches for JNLPBA dataset.