# A Comparative Analysis of Active Learning for Biomedical Text Mining
## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**FastText** & **HoC** & **without** & **RS** & **LC** & **IDD** & **Margin** & **MRD** \\ \hline \multirow{3}{*}{SVM} & Accuracy & 79.51 & 79.37 & 77.64 & 78.99 & 79.34 & 79.06 \\ \cline{2-7}  & F1 & 88.35 & 87.82 & 84.07 & 86.85 & 87.97 & 85.73 \\ \hline \multirow{3}{*}{NB} & Accuracy & 79.58 & 79.55 & 79.46 & 79.58 & 79.53 & 79.54 \\ \cline{2-7}  & F1 & 88.61 & 88.56 & 88.27 & 88.63 & 88.44 & 88.45 \\ \hline \multirow{3}{*}{KNN} & Accuracy & 74.64 & 75.86 & 75.52 & 74.92 & 69.05 & 75.85 \\ \cline{2-7}  & F1 & 78.44 & 81.21 & 80.94 & 79.87 & 70.67 & 81.11 \\ \hline \multirow{3}{*}{XGBoost} & Accuracy & 79.61 & 79.57 & 79.62 & 79.65 & 79.51 & 79.63 \\ \cline{2-7}  & F1 & 88.29 & 88.41 & 88.37 & 88.47 & 88.43 & 88.54 \\ \hline \multirow{3}{*}{Random forest} & Accuracy & 67.89 & 68.41 & 76.20 & 74.21 & 76.39 & 77.73 \\ \cline{2-7}  & F1 & 68.68 & 69.73 & 82.43 & 79.06 & 82.49 & 84.66 \\ \hline \multirow{3}{*}{AdaBoost} & Accuracy & 73.69 & 68.49 & 77.13 & 74.30 & 71.35 & 76.33 \\ \cline{2-7}  & F1 & 78.62 & 69.93 & 84.57 & 80.24 & 75.87 & 82.92 \\ \hline \multirow{3}{*}{CatBoost} & Accuracy & 79.57 & 79.53 & 79.62 & 79.58 & 78.46 & 79.59 \\ \cline{2-7}  & F1 & 88.33 & 88.16 & 88.37 & 88.32 & 86.13 & 88.42 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of results with and without AL methods (FastText) for ChemProt dataset. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**FastText** & **DDI** & **without** & **RS** & **LC** & **IDD** & **Margin** & **MRD** \\ \hline \multirow{3}{*}{AdaBoost} & Accuracy & 70.58 & 66.08 & 82.47 & 90.61 & 78.07 & 78.84 \\ \cline{2-7}  & F1 & 70.25 & 63.24 & 89.71 & 82.94 & 82.09 & 83.43 \\ \hline \multirow{3}{*}{CatBoost} & Accuracy & 81.17 & 81.27 & 82.48 & 81.46 & 82.17 & 82.57 \\ \cline{2-7}  & F1 & 85.24 & 86.40 & 88.36 & 87.35 & 87.95 & 88.69 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._ \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**BERT** & **HoC** & **without** & **RS** & **LC** & **IDD** & **Margin** & **MRD** \\ \hline \multirow{3}{*}{SVM} & Accuracy & 82.24 & 83.37 & 82.95 & 81.64 & 79.78 & 83.72 \\ \cline{2-7}  & F1 & 86.59 & 89.57 & 85.58 & 85.53 & 81.94 & 89.48 \\ \hline \multirow{3}{*}{NB} & Accuracy & 62.59 & 62.70 & 67.87 & 65.51 & 66.22 & 42.15 \\ \cline{2-7}  & F1 & 58.53 & 58.72 & 66.18 & 62.75 & 64.05 & 32.30 \\ \hline \multirow{3}{*}{KNN} & Accuracy & 73.77 & 75.51 & 73.55 & 74.90 & 73.44 & 73.81 \\ \cline{2-7}  & F1 & 74.20 & 76.92 & 74.12 & 76.04 & 73.94 & 74.54 \\ \hline \multirow{3}{*}{XGBoost} & Accuracy & 83.09 & 82.59 & 82.78 & 82.97 & 83.09 & 82.56 \\ \cline{2-7}  & F1 & 88.85 & 88.73 & 88.11 & 88.99 & 88.41 & 88.21 \\ \hline \multirow{3}{*}{Random forest} & Accuracy & 75.89 & 78.93 & 79.43 & 78.25 & 79.90 & 79.43 \\ \cline{2-7}  & F1 & 77.80 & 82.30 & 83.64 & 81.87 & 84.37 & 82.78 \\ \hline \multirow{3}{*}{AdaBoost} & Accuracy & 82.59 & 74.85 & 81.74 & 81.93 & 81.53 & 79.76 \\ \cline{2-7}  & F1 & 89.80 & 76.37 & 87.41 & 88.39 & 87.91 & 84.87 \\ \hline \multirow{3}{*}{CatBoost} & Accuracy & 81.11 & 81.03 & 81.88 & 82.16 & 82.45 & 81.91 \\ \cline{2-7}  & F1 & 85.85 & 85.14 & 86.59 & 87.80 & 87.79 & 86.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of results with and without AL methods (BERT) for DDI dataset.