# On the Use of Knowledge Transfer Techniques for Biomedical Named Entity Recognition +
## 4 Results and Discussion of MTM-CNN  
As a first step, a single-task model (STM-CNN) was implemented for all 15 datasets mentioned earlier. Afterward, the MTM-CNN was trained with all 15 datasets in an MTL approach. Table 1 depicts the comparison between the results of the MTM-CNN and its counterpart the STM-CNN. Each experiment was run 10 times, and the average F1-score of those 10 runs is reported in this section and the best results are shown in bold font.  
We see that, for most of the datasets, the results improved markedly by using the MTM-CNN with an absolute average difference of F1-score up to 2.1%, showing the importance of the MTL approach in BioNER. More interestingly, the F1-score for BioNLP13 GE increased from 73.3 to 79.8. The BC2 GM, BC4 CHEMD, CRAFT, and JNLPBA showed a performance degradation with the MTL approach. One possible reason could be the size of these datasets. The size of these datasets was bigger than the rest of the other datasets. For this reason, a performance increase was noticed for those datasets that had a small number of entity annotations. This can be seen for Ex-PTM which had a small number of entities and showed a noticeable improvement, increasing to 80.9 from 72.7 in F1-score, with the MTL approach. These results suggested that the MTM-CNN improved the performance of those datasets which did not have many examples. The results illustrated that the MTM-CNN could learn complex features that were difficult to learn in an STM-CNN.  
Table 2 shows the comparison of various STM-CNNs with respect to state-of-the-art STMs. Conclusively, our STM-CNN showed an average F1-score gain of up to 4% compared to that in [34], while against [PERSON] et al. [35], our STM-CNN model depicted an average F1-score increase pf up to 2%. For most of the datasets, the STM-CNN showed a performance gain compared to other approaches while the model proposed by [PERSON] et al. [35] illustrated a performance increase on four of these datasets. A prominent increase can be noticed for BioNLP09, where the STM-CNN raised the F1-score from 84.2 to 87.0 against that of the model proposed by [PERSON] et al. [35] The model proposed by [PERSON] et al. [34] did not exhibit any improvement on any dataset; their model [34] is CNN-based and it also does not consider the character-level information which may lead to the out-of-vocabulary problem. This might be the reason that this model failed to show a performance gain compared to the results of [PERSON] et al. and the MTM-CNN.  
The comparison of various MTMs is illustrated in Table 3. We observe that the MTM-CNN model outperformed the model proposed by [PERSON] et al. [34] with an absolute increase of the average F1-score of up to 4%. It is worth noticing that for BC2 GM, the F1-score rose to 80.8 from 73.2 and for BioNLP11 ID, the F1-score improved from 81.7 to 87.7. Compared to the multitask model presented by [PERSON] et al. [35], the MTM-CNN showed  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Datasets** & **STM-CNN** & **MTM-CNN** \\ \hline AnatEM & 85.8 & **86.9** \\ BC2 GM & **80.9** & 80.8 \\ BC4 CHEMD & **88.6** & 87.3 \\ BC5 CDR & 85.6 & **87.8** \\ BioNLP09 & 87.0 & **88.7** \\ BioNLP11 EPI & 81.4 & **84.7** \\ BioNLP11 ID & 83.2 & **87.6** \\ BioNLP13 CG & 81.2 & **84.2** \\ BioNLP13 GE & 73.3 & **79.8** \\ BioNLP13 PC & 86.3 & **88.8** \\ CRAFT & **83.8** & 83.1 \\ Ex-PTM & 72.7 & **80.9** \\ JNLPBA & **74.4** & 74.0 \\ LINNAEUS & 87.3 & **87.7** \\ NCBI-disease & 84.1 & **85.6** \\ Average & 82.4 & **84.5** \\ \hline \hline \end{tabular}
\end{table}
Table 1: STM vs. MTM-CNN.  
an average increase of F1-score of 0.8%. The most prominent increase in F1-scores was noted for BioNLP11 EPI (+1.6%), BioNLP11 ID (+4.4%), BioNLP13 CG (+1.8%), and JNLPBA (+1.9%) against [PERSON] et al. [35]