# Reversal of the Word Sense Disambiguation Task Using a Deep Learning Model
## 3 Training  
The principal objective of this project was to develop a model capable of achieving accuracy comparable with existing models utilizing embedded word vectors, as demonstrated in [17; 18; 19]. A key innovation pursued from inception was the imperative for the model to significantly reduce its computational memory footprint while maintaining such levels of accuracy. Following the successful achievement of predefined targets related to the model's size and accuracy, an exhaustive investigation was undertaken to dissect the underlying causes of error propagation in the context of word sense disambiguation. Leveraging established datasets [17; 20; 21; 22; 23], our analysis revealed that certain inaccuracies did not originate from inherent deficiencies in the model but rather from distinct sources, as follows. 1. **Discrepancies in the test dataset:** The first source of error was discrepancies within the test datasets themselves. Variability in the quality of annotation, the distribution of the data, and contextual diversity posed challenges for accurate disambiguation. In Figure 3 (left), we observe a sample of test data associated with the synset value \"level%1:26:00::_test\", which exhibits proximity to the group of training data represented by the synset value \"level%1:07:00::\". Further investigation using tools from the WSD debugging system revealed that this discrepancy arose from an annotation error in the standard test data [23]. Specifically, the sentence with the identifier \"semeval2013.d000.s008\" contained the phrase \"emissions... compared with 1990 levels\". Here, the model identified a possible misinterpretation of \"levels\" and assigned it the sense of \"a position on a scale of intensity or amount or quality\" instead of \"a specific identifiable position in a continuum or series or especially in a process\". The analyst reviewed the model's proposed corrections and, if in agreement, incorporated them into the \"sentence_synset_to_synset.tsv\" corrections file (available on the project's GitHub page). On the right side of Figure 3, we present an image reflecting these corrected annotations. 2. **Annotation errors in the training data:** Secondly, we identified annotation errors within the training dataset [24]. These inaccuracies were propagated through the model, affecting its performance during inference. Addressing and rectifying such errors became crucial for enhancing the model's robustness. Similar to the test dataset, the training data also exhibited annotation errors. We identified these errors by visually inspecting NCA projections of the data points. Points that deviated significantly from their designated synset cluster were flagged for potential misannotation. Fol  
Figure 3: This figure showcases the impact of the proposed data transformation methodology on word sense disambiguation. Each point on the (**left**) side represents a sentence containing the noun “level” from the standard WordNet dataset. The (**right**) side depicts the corresponding data points after applying the transformations outlined in the study. By visually comparing the points, we can observe how the transformations effectively separate data points belonging to different senses of the word “level”, improving the model’s ability to distinguish between them during disambiguation. lowing the analyst's verification of the context and intended sense, corrections were incorporated into the \"sentence_synset_to_synset.tsv\" file for retraining the model. 3. **Limitations of WordNet synsets:** Our investigation highlighted inadequacies within WordNet synsets' relationships and descriptions. While WordNet serves as a valuable lexical resource, it occasionally fails to capture subtle nuances required for comprehensive word sense disambiguation. Notably, we observed an overly granular segmentation of certain words into synsets, hindering accurate resolution of their sense. Therefore, when modifying WordNet synset lists or relations, it is crucial to consider the intended application of natural language processing.