## 4 Results
### Comparative Analysis of Model Accuracies in WSD  
Based on the models' stated accuracy scores (61%, 79%, 74%, 70%, 64%, 62%, and 63%) as well as their intrinsic qualities, Figure 9 and Table 2 provide a thorough comparison of the models. Recall that long short-term memory (LSTM) is a kind of recurrent neural network (RNN) created to deal with long-term dependencies in sequence data. It is especially well suited for applications like sequence labeling, language modeling, and time series prediction. Although the model learns from the data, its ability to recognize intricate patterns may be constrained, as indicated by its 61% accuracy rate. This is to be expected since LSTM models generally perform worse than transformer models, despite being strong for sequential tasks. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Accuracy** & **Key Strengths** & **Key Weaknesses** \\ \hline RNN-LSTM & 61\% & Good for sequence learning, handles & Limited compared to \\  & & long dependencies & transformer models \\ \hline BiGRU & 79\% & Bidirectional, efficient, high accuracy & \begin{tabular}{c} RNN-based, may underperform \\ on complex tasks \\ \end{tabular} \\ \hline LSTMLM & 74\% & Effective for language modeling & \begin{tabular}{c} May not be as strong in other \\ NLP tasks \\ \end{tabular} \\ \hline DeBERTa & 70\% & \begin{tabular}{c} State-of-the-art, better \\ context handling \\ \end{tabular} & \begin{tabular}{c} Larger, more \\ computationally expensive \\ \end{tabular} \\ \hline DistilBERT & 64\% & \
\begin{tabular}{c} Faster, resource-efficient, good for \\ real-time applications \\ \end{tabular} &
\begin{tabular}{c} Poorer performance than full \\ BERT models \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary table of baseline model performances. A type of GRU (Gated Recurrent Unit) that can analyze input data in both the forward and backward directions is called a BiGRU (Bidirectional Gated Recurrent Unit). Among the models on the list, BiGRU performs the best, with an accuracy of 79%. Its bidirectional nature, which enables the model to learn from both past and future contexts, is responsible for its performance. An LSTM model intended for language modeling (LM) goals, such as next-word prediction, text production, or sentence structure comprehension, is called an LSTMLM. The LSTMLM performs well on language modeling tasks with an accuracy of 74%, but it lags behind BiGRU. This could mean that transformer-based models beat LSTM-based models in terms of accuracy, even while LSTM-based models are effective for sequence learning. DistilBERT is a condensed, more effective variant of BERT that is 60% faster and uses fewer resources while maintaining 97% of BERT's language comprehension abilities. Disentangled attention and improved position embeddings are two advancements that are incorporated into DeBERTa (decoding-enhanced BERT with disentangled attention), an upgraded version of BERT. With an accuracy of 70%, DeBERTa appears to perform well, but it not the best model on the list. It is crucial to remember that DeBERTa's architecture enables it to perform quite well on a variety of downstream tasks, and that 70% accuracy can be the result of particular task-related difficulties rather than a basic flaw in the model. With an accuracy of 64%, DistilBERT is a little less accurate than one might anticipate for a transformer model. However, in contexts with limited resources or where rapid deployment is necessary, DistilBERT may be worth the trade-off because it sacrifices some capacity for speed and efficiency. A single text-to-text model called T5 was created to handle a broad range of natural language processing tasks by transforming them into text-to-text formats. T5 appears to perform worse than the other models, such as BiGRU and LSTMLM, with an accuracy of 62%. The task's nature or the T5 fine-tuning procedure may be to blame for this. Nonetheless, the model's ability to learn from the context of the LSTM-based models is not a good choice.