## 4 Experiments
### Results and Discussion
The confusion matrix in Figure 11 shows that across all models, the majority of the misclassifications in the confusion matrix were caused by incorrect predictions of B-PRODUCT against all the entities, except for I-UNIT. On the contrary, I-UNIT has zero misclassifications against all the models' entities. Overall, based on the performance results shown in Tables 6 and 7, as well as on the confusion matrix in Figure 11, it is suggested that RNE can be the method of choice for NER. It can be seen that our proposed model (RNE) improves the model's performance in detecting ingredient entities by 0.2% to 0.5% compared to the single models. However, the computational cost shown in Figure 10 suggests that given the model's simplicity and computational time, a single model such as Bi-LSTM could be the second-best option after ERN. Although Bi-GRU performs marginally better, Bi-LSTMs are faster at training and computationally less expensive than ERN. In addition, it is critical to emphasize that there is no assurance that the combination of multiple classifiers will always outperform the ensemble's best individual classifier except for certain cases [63]. Consequently, combining classifiers may not necessarily outperform the best classifier in the ensemble, but it does reduce the risk of making a particularly poor selection. Figure 10: Computational cost comparison. Figure 11: Confusion matrix of each model. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multicolumn{3}{c}{**Bi-RNN**} & \multicolumn{3}{c}{**Bi-GRU**} & \multicolumn{3}{c}{**Bi-LSTM**} & \multicolumn{3}{c}{**RNE**} \\ \cline{2-13}  & **P** & **R** & **F1** & **P** & **R** & **F1** & **P** & **R** & **F1** & **P** & **R** & **F1** \\ \hline B-INGREDIENT & 0.948 & 0.971 & 0.960 & 0.954 & 0.974 & 0.964 & 0.956 & 0.971 & 0.964 & **0.958** & **0.977** & **0.968** \\ B-PRODUCT & 0.887 & 0.935 & 0.910 & 0.890 & 0.943 & 0.916 & 0.885 & 0.941 & 0.912 & **0.891** & **0.946** & **0.917** \\ B-QUALITY & **0.977** & 0.964 & 0.970 & 0.967 & **0.976** & 0.972 & 0.971 & 0.971 & 0.971 & 0.975 & **0.976** & **0.975** \\ B-STATE & **0.999** & **0.999** & **0.999** & **0.999** & **0.999** & **0.999** & **0.999** & **0.999** & **0.999** & **0.999** & **0.999** & **0.999** & **0.999** & **0.999** \\ B-UNIT & 0.995 & **0.998** & 0.996 & **0.997** & 0.997 & **0.997** & 0.995 & **0.998** & **0.997** & **0.997** & 0.997 & 0.997 \\ I-INGREDIENT & 0.970 & 0.969 & 0.996 & 0.978 & 0.969 & 0.974 & 0.971 & 0.971 & 0.971 & **0.979** & **0.974** & **0.977** \\ I-PRODUCT & 0.937 & 0.968 & 0.952 & 0.951 & 0.965 & 0.958 & 0.947 & 0.961 & 0.954 & **0.952** & **0.970** & **0.961** \\ I-QUALITY & 0.911 & 0.960 & 0.935 & 0.936 & 0.959 & 0.947 & 0.928 & 0.961 & 0.945 & **0.937** & **0.967** & **0.951** \\ I-STATE & **0.943** & 0.951 & 0.947 & 0.922 & 0.958 & 0.940 & 0.906 & 0.961 & 0.933 & 0.937 & **0.968** & **0.953** \\ I-UNIT & **1.000** & **1.000** & **1.000** & **1.000** & **1.000** & **1.000** & 0.998 & **1.000** & 0.999 & **1.000** & **1.000** & **1.000** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The classification report for each NER model with their best performance highlighted in **bold**. Note that **P** is precision, **R** is recall, and **F1** is F1 score.