## 4 Results
### Comparative Analysis of Model Accuracies in WSD
Figure 9: Comparative analysis of model accuracies in WSD. less, T5's adaptability to various NLP tasks continues to be one of its main advantages. By sharing parameters between layers, ALBERT, a more parameter-efficient variant of BERT, is intended to preserve excellent performance while minimizing model size. With a 63% accuracy rate, ALBERT offers a fair balance between performance and efficiency. Despite not achieving the best accuracy, it is a good option for contexts with limited resources due to its lower computing requirements. Tables 3 and 4 are contingency tables for each model pair. McNemar's Test was computed to check if the accuracy differences are statistically significant; confidence intervals were then computed for each model comparison. The two models, RNN-LSTM and DistilBERT, in Tables 3 and 4, express a \(p\)-value of 0.07010, which proves that the difference between the models is not statistically significant, whereas other models in Table 3 show a \(p\)-value < 0.05, proving that the difference between the models is statistically significant. The statistical significance of the model differences is revealed by the \(p\)-values and confidence ranges. While the confidence interval gives a range that the genuine difference most likely falls within, a lower \(p\)-value (usually less than 0.05) indicates a significant difference. In particular, when compared to BiGRU (ADAM), BiGRU (Attention), and Hybrid BiGRU + BERT, comparisons involving Hybrid BiGRU + RoBERTa frequently show highly significant differences (\(p\)-value = 0.00000), with the confidence intervals showing continuously worse performance. Similarly, when compared to Hybrid BiGRU + RoBERTa, Hybrid BiGRU + DeBERTa and Hybrid BiGRU + ALBERT demonstrate great statistical significance, indicating their superior performance. In comparing BiGRU (ADAM) and BiGRU (Attention), Hybrid BiGRU + DeBERTa and Hybrid BiGRU + ALBERT, and Hybrid BiGRU + DeBERTa and BiGRU (Attention), on the other hand, results in higher \(p\)-values (>0.05), indicating that there are no statistically significant differences. The confidence intervals for these comparisons overlap considerably, reinforcing the similarity in performance. These results suggest that Hybrid BiGRU models leveraging DeBERTa and ALBERT embeddings perform better and are statistically distinct from those \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model 1** & **Model 2** & **Statistic** & \(p\)**-Value** & **Confidence Interval** \\ \hline RNN-LSTM & BiGRU & 122.000 & 0.00000 & (0.6830067283444499, 0.7662032039354598) \\  & & & & (0.6369521078503567, 0.7228712917081422) \\ RNN-LSTM & DeBERTa & 188.000 & 0.00001 & (0.557530428504149, 0.6458594020043255) \\ RNN-LSTM & DistilBERT & 212.000 & 0.07010 & (0.49777829086492364, 0.5884286056868006) \\ BiGRU & LSTMLM & 162.000 & 0.06494 & (0.3986093107729891, 0.5013906892270109) \\ BiGRU & DeBERTa & 148.000 & 0.00000 & (0.323529705809955, 0.41832493078152366) \\ BiGRU & DistilBERT & 133.000 & 0.00000 & (0.2688570801976323, 0.3570252727435441) \\ LSTMLM & DeBERTa & 181.000 & 0.00141 & (0.37517803244141085, 0.46864481138143294) \\ LSTMLM & DistilBERT & 153.000 & 0.00000 & (0.3113578957374595, 0.4019709237129674) \\ DeBERTa & DistilBERT & 207.000 & 0.01110 & (0.3955443431952622, 0.48530672063452507) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Contingency table for base models. using RoBERTa, while attention-based BiGRU models do not significantly outperform their ADAM-optimized counterparts. Enhanced Models  
When evaluating the improved performance of different models, as shown in Figure 10 and Table 5, it is critical to compare their accuracy metrics with the methods used for enhancement, particularly for models that have been optimized using different techniques like Adam optimization, attention mechanisms, or hybrid models. A thorough examination of the models based on the updated accuracy scores and improvements made to them is provided below.