# 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
**ELMo** & **DDI** & **without** & **RS** & **LC** & **IDD** & **Margin** & **MRD** \\ \hline \multirow{3}{*}{SVM} & Accuracy & 83.01 & 83.01 & 83.01 & 83.01 & 83.01 & 83.01 \\ \cline{2-7}  & F1 & 90.71 & 90.71 & 90.71 & 90.71 & 90.71 & 90.71 \\ \hline \multirow{3}{*}{NB} & Accuracy & 38.21 & 44.61 & 50.56 & 55.06 & 56.22 & 56.29 \\ \cline{2-7}  & F1 & 28.45 & 36.23 & 48.32 & 47.97 & 50.28 & 49.46 \\ \hline \multirow{3}{*}{KNN} & Accuracy & 78.43 & 79.60 & 79.33 & 80.56 & 79.12 & 80.78 \\ \cline{2-7}  & F1 & 79.11 & 82.54 & 81.26 & 84.26 & 82.77 & 84.38 \\ \hline \multirow{3}{*}{XGBoost} & Accuracy & 82.38 & 83.13 & 82.17 & 82.79 & 83.16 & 82.87 \\ \cline{2-7}  & F1 & 88.23 & 90.20 & 88.65 & 90.62 & 90.66 & 89.94 \\ \hline \multirow{3}{*}{Random forest} & Accuracy & 80.82 & 79.92 & 80.43 & 80.32 & 81.41 & 80.85 \\ \cline{2-7}  & F1 & 85.31 & 84.44 & 86.21 & 85.10 & 87.06 & 86.15 \\ \hline \multirow{3}{*}{AdaBoost} & Accuracy & 77.56 & 78.01 & 79.76 & 78.15 & 80.99 & 80.16 \\ \cline{2-7}  & F1 & 81.32 & 82.40 & 83.53 & 82.45 & 86.53 & 85.32 \\ \hline \multirow{3}{*}{CatBoost} & Accuracy & 80.45 & 82.42 & 81.43 & 82.97 & 83.02 & 82.90 \\ \cline{2-7}  & F1 & 87.34 & 88.71 & 89.54 & 90.35 & 90.42 & 89.34 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Comparison of results with and without AL methods (ELMo) for DDI dataset. The result shows a table DDI dataset, which applies BERT for feature extraction and has the best performance in accuracy when applying an SVM algorithm with an AL framework that builds based on MRD query strategies. Almost all ML methods have good performance except KNN algorithms. Furthermore, in general, AL algorithms have slightly better performance than passive learning algorithms. HoC datasets have a much clearer difference between different methods applied. In general, AL performs better than passive learning algorithms. For ML algorithms, we can see that XGBoost and CatBoost have relatively better performance than others. Furthermore, for query strategies, margins have overall better performance than others. The following are answers to our research questions. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**ELMo** & **ChemProt** & **without** & **RS** & **LC** & **IDD** & **Margin** & **MRD** \\ \hline \multirow{3}{*}{SVM} & Accuracy & 79.59 & 79.59 & 79.59 & 79.59 & 79.59 & 79.59 \\ \cline{2-8}  & F1 & 88.63 & 88.64 & 88.64 & 88.64 & 88.64 & 88.64 \\ \hline \multirow{3}{*}{NB} & Accuracy & 37.77 & 38.59 & 48.91 & 50.18 & 50.46 & 45.55 \\ \cline{2-8}  & F1 & 28.23 & 29.20 & 41.98 & 42.98 & 43.42 & 37.91 \\ \hline \multirow{3}{*}{KNN} & Accuracy & 62.52 & 64.89 & 63.57 & 63.76 & 62.34 & 67.18 \\ \cline{2-8}  & F1 & 60.38 & 63.78 & 62,08 & 62,64 & 60.08 & 67.35 \\ \hline \multirow{3}{*}{XGBoost} & Accuracy & 79.83 & 79.72 & 79.96 & 79.65 & 79.81 & 79.75 \\ \cline{2-8}  & F1 & 88.00 & 87.56 & 87.26 & 87.47 & 87.27 & 87.45 \\ \hline \multirow{3}{*}{Random forest} & Accuracy & 74.82 & 75.38 & 77.42 & 73.49 & 77.60 & 76.97 \\ \cline{2-8}  & F1 & 80.22 & 80.87 & 83.68 & 77.86 & 84.28 & 84.00 \\ \hline \multirow{3}{*}{AdaBoost} & Accuracy & 72.88 & 68.11 & 77.02 & 75.58 & 76.72 & 75.50 \\ \cline{2-8}  & F1 & 76.40 & 69.51 & 83.92 & 81.45 & 82.68 & 76.88 \\ \hline \multirow{3}{*}{CatBoost} & Accuracy & 76.78 & 76.23 & 78.45 & 78.34 & 78.79 & 76.94 \\ \cline{2-8}  & F1 & 82.01 & 81.92 & 84.61 & 84.92 & 85.17 & 83.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Comparison of results with and without AL methods (ELMo) for ChemProt dataset.