# Reversal of the Word Sense Disambiguation Task Using a Deep Learning Model
## 5 Results  
This work initially focused on enriching the SemCor dataset with WordNet gloss descriptions to enhance semantic representation. The core objective was to develop a WSD model with two key properties: (1) a significantly reduced memory footprint compared with the existing deep learning approaches, ensuring compatibility with resource-constrained environments such as Google Colab; and (2) state-of-the-art accuracy on benchmark datasets such as SemEval. This goal was achieved through a novel method that leverages NCA for reducing the dimensionality and incorporates a vector quantization step, where embedded vector values are rounded to a predefined precision (e.g., two decimal places). Following the development of the initial model, a detailed error analysis was conducted to investigate the misclassification of a significant number of synsets. This analysis revealed two key issues: (1) the presence of errors in sentence annotations within the training data, and (2) inconsistencies in the synsets' assignments for semantically similar words within WordNet. To address these shortcomings, we developed a WordNet debugging tool specifically designed to identify annotation errors, and proposed modifications for improved alignment between the embedded vector clusters and the WordNet structure. This comprehensive approach not only led to the creation of a novel WSD system but also resulted in the development of an updated WordNet version, ensuring compatibility with the latest iteration. A crucial modification involved the merging of synonymous synsets to maximize the overlap between embedded vector representations and their corresponding WordNet concepts. To achieve the first project objective of reducing the memory footprint while maintaining accuracy, we investigated techniques of reducing the dimensionality. Notably, the size of the embedded vector base was compressed from 3 gigabytes to 100 megabytes, representing a significant reduction. Interestingly, this compression not only preserved the model's accuracy but also led to a slight improvement in the F1 scores (Table 1). To evaluate the model's performance, we used the Senseval All test dataset [18], encompassing five subdatasets from 2001 to 2015 (Senseval-2 [20], Senseval-3 [21], SemEval-07 [22], SemEval-13 [23], and SemEval-15 [17]). These data are readily available on the project's GitHub repository in a tab-separated values (.tsv) format, where each line represents a synset's test value with its corresponding sentence example. The training data comprised two components: (1) SemCor [24], a manually sense-annotated corpus containing 226,040 annotations across 352 documents; and (2) WordNet synsets' meanings and definitions, curated by removing outliers identified by the WSD debugging system on the basis of their distance from the class centers in the NCA projection space (details of \"bad\" definitions are provided in the GitHub repository). Notably, the training data are dynamic and evolve as the WSD model is refined. This unexpected finding in Table 1 (BERT-NCA k-NN was better than BERT\({}_{1024}\) 1-NN) suggests the effectiveness of the NCA component model in mitigating noise in the data during the projection of embedding vectors from a high-dimensional space (1024 dimensions) to a lower-dimensional space (ranging from two to five dimensions). Table 1 summarizes \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **POS** & **Senseval All** \\ \hline \multirow{2}{*}{MFS} & Verb & 49.6 \\  & Noun & 67.5 \\ \hline \multirow{2}{*}{BERT-2 NCA 1-NN} & Verb & 61.7 \\  & Noun & 74.1 \\ \hline \multirow{2}{*}{BERT-NCA k-NN} & Verb & 64.4 \\  & Noun & 76.7 \\ \hline \multirow{2}{*}{BERT\({}_{1024}\) 1-NN} & Verb & 63.9 \\  & Noun & 76.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results of WordNet sense disambiguation (F1 score). the F1 scores achieved by various models using the unmodified relationships of WordNet synsets.