# On the Use of Knowledge Transfer Techniques for Biomedical Named Entity Recognition +
## 7 Results and Discussion for ELMo  
The comparison of different models trained with various approaches of ELMo contextual embeddings is shown in Table 8. It can be seen that the STM\({}_{EL}\) (the STM trained with ELMo embeddings) performed better compared with the MTM\({}_{EL}\) (the MTM trained with ELMo embeddings) with average F1-score gain of 0.2%. It is observed that the STM\({}_{EL}\) generated an F1-score of 91.3 for BC4 CHEMD which dropped to 88.6 when the MTM\({}_{EL}\) was used. It can be hypothesized that the performance drop from the STM to the MTM can be due to the entity ambiguity and prevalence of unseen entity (the probability of confronting a word never seen during training). We speculate that the performance of the MTM\({}_{EL}\) dropped as it could not overcome the ambiguity problem that occurred during the MTL approach. Additionally, the MTL approach also performed implicit data augmentation which decreased the prevalence of the words (ELMo saw huge numbers of words in the MTL approach). More specifically, the performance of the contextual embeddings dropped for BioNER using MTL as it increased entity ambiguity and decreased prevalence. However, when performing the transfer learning approach (fine-tuning the MTM\({}_{EL}\) for each specific dataset), the performance of the STM (\(MTM^{\textit{exp}}_{EL}\)\(STM_{EL}\)) increased with an average F1-score gain of 0.6%, as again, we can assume that the entity ambiguity decreased and the prevalence increased for contextual embeddings during the STL approach. It is worth noticing that the \(MTM^{\textit{exp}}_{EL}\)\(STM_{EL}\) increased the F1-score for BC4 CHEMD to 91.1 from 88.6 obtained by the MTM\({}_{EL}\). We emphasize that the use of contextual embedding (ELMo) still enhanced the performance of the model in the MTL approach which we can observe in Table 9. The results of different earlier proposed models are compared with the ELMo-based MTM (MTM\({}_{EL}\)). The fourth column (MTM) shows the same as the MTM\({}_{EL}\) column but w  
Figure 11: Post hoc Friedman test output for \(MTM\)\(\rightarrow\)\(STM\)  
Figure 12: Graphical representation of Friedman test ranks produced by \(MTM\)\(\rightarrow\)\(STM\). Models are shown according to their ranks starting with the best model from left to right. of the ELMo embeddings. We can instantly discover that by integrating the contextual embedding, the performance of the MTM increased substantially. Furthermore, the results of the MTM\({}_{EL}\) showed a noticeable increase compared to the other MTMs (the MTM-CNN and MTM-CW proposed earlier). In Table 10, we also compare the fine-tuning approach for ELMo with our earlier fine-tuning approach (Section 6). We see that \(MTM^{\textit{smf}}_{EL}\)\(STM_{EL}\) achieved distinguishable performance compared with our previous proposed approach. The \(MTM^{\textit{smf}}_{EL}\)\(\xrightarrow{\textit{STM}_{EL}}\) increased the F1-score for 13 out of 15 datasets. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Datasets** & **MTM\({}^{\textit{lab}}_{EL}\) STM** & **MTM\({}^{\textit{lab}}_{EL}\) STM** & **MTM\({}^{\textit{smf}}_{EL}\) STM** & **MTM\({}^{\textit{smf}}_{EL}\) STM\({}_{EL}\)** \\ \hline AnatEM & 87.9 & 88.0 & 88.0 & **89.5** \\ BC2 GM & 82.1 & 82.2 & 82.0 & **83.1** \\ BC4 CHEMD & 89.9 & 90.4 & 90.4 & **91.1** \\ BC5 CDR & 88.8 & 89.0 & 89.1 & **90.0** \\ BioNLP09 & 88.5 & 88.7 & 88.5 & **89.9** \\ BioNLP11 EPI & 85.3 & 85.5 & 85.4 & **87.7** \\ BioNLP11D & 87.6 & 87.8 & 87.9 & **88.0** \\ BioNLP13 CG & 84.9 & 85.2 & 85.1 & **87.1** \\ BioNLP13 CE & 80.1 & 80.1 & 80.2 & **82.1** \\ BioNLP13 PC & 89.3 & 89.2 & 89.3 & **90.5** \\ CRAFT & 84.9 & 85.3 & 85.0 & **86.9** \\ ExPTM & 81.7 & 82.0 & 81.8 & **83.8** \\ INLPBA & **73.0** & 72.1 & 71.9 & 72.8 \\ LINNAEUS & **88.8** & 88.2 & **88.8** & 88.2 \\ NCBI & 86.2 & 85.9 & 86.2 & **86.7** \\ Average & 85.3 & 85.3 & 85.3 & **86.5** \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Results comparison of transfer Learning for different models with and without ELMo.