## 4 Results
### Evaluation Metrics  
A series of performance criteria was employed to analyze the enhanced BiGRU model at various developmental phases, providing a comprehensive evaluation of the model's efficacy in word meaning disambiguation (WSD). The accuracy, precision, recall, and F1-score were employed to evaluate each model iteration, as they jointly demonstrate the model's efficacy in differentiating between several interpretations of ambiguous terms within a certain context. This is represented numerically in Equation (16):  
\[\text{Accuracy}=\frac{\text{Correct Predictions}}{\text{Total Predictions}} \tag{16}\]  
This metric is essential for understanding the model's general performance in predicting the correct word sense without additional enhancements. The accuracy of each subsequent model is compared against this baseline to assess improvements.  
Precision and recall are particularly relevant for WSD tasks, where accuracy alone may not capture the nuances of identifying the correct sense among multiple candidates. Precision measures the proportion of correctly predicted senses among those predicted as positive by the model, reflecting its ability to avoid false positives. The precision metric is calculated using Equation (17):  
\[\text{Precision}=\frac{\text{True Positives}}{\text{True Positives}+\text{ False Positives}} \tag{17}\]  
Recall, conversely, assesses the model's ability to recognize all pertinent instances of each word sense. This statistic indicates the model's sensitivity to ambiguous circumstances by quantifying the ratio of accurately predicted senses to the total real senses, derived using Equation (18):  
\[\text{Recall}=\frac{\text{True Positives}}{\text{True Positives}+\text{ False Negatives}} \tag{18}\]  
Figure 8: Outlier detection.  
For each model variation, the F1-score is calculated to equilibrate precision and recall, which is especially beneficial in cases of an unequal distribution of word senses or when specific senses are more difficult to forecast precisely. The F1-score integrates both measurements as the harmonic mean, providing a singular measure to assess the model's efficacy in accurately and consistently disambiguating sensations. This is delineated in Equation (19):  
\[\text{F1-score}=2\times\frac{\text{Precision}\times\text{Recall}}{\text{ Precision}+\text{Recall}} \tag{19}\]  
Compared to that of the baseline BiGRU, performance is then evaluated for the BiGRU with an attention model, which incorporates an attention mechanism to selectively emphasize relevant context words for WSD. The BiGRU with attention + transformer embeddings model adds transformer-based embeddings to further capture contextual nuances, and metrics for this model show how pre-trained contextual embeddings impact word sense identification.  
These metrics, stored and compared across each model variant, provide an in-depth look at the contributions of each architectural enhancement. The progression of the accuracy, precision, recall, and F1-score in each version of the enhanced BiGRU highlights the incremental improvements achieved by refining context representation and integrating syntactic features. Through this iterative evaluation, the final model's performance demonstrates how each component optimally contributes to addressing the challenges of WSD.