# Reversal of the Word Sense Disambiguation Task Using a Deep Learning Model
## 3 Training
In this work, synset merging decisions were guided by the ultimate goal of using NLP to generate 2D and 3D models, as described in [1]. Over the past five decades, starting with the pioneering SHRDLU system [25], numerous systems have attempted natural language manipulation of objects of computer graphics (see [26] for a review of 26 such systems). Many of these systems process multiple sentences to identify physical objects within a 3D scene, often leveraging spatial knowledge to resolve ambiguities (e.g., SceneSever [27]). This specific NLP application guided the WSD solution presented in this study. Returning to the example depicted in Figure 3, we made the decision to merge the synsets \"level%1:26:00::\", \"level%1:09:00::\", and \"level%1:26:01::\" into a single group. Subsequently, Figure 3 (right) illustrates how the training sample points align in the plane defined by the two NCA components. Throughout the course of this WSD project, approximately 1800 such changes were implemented, and all details are accessible on the project's GitHub repository in the file \"synset_to_synset.tsv\". 4. **Scarcity of Data for Specific Words:** Another critical constraint emerged: the lack of sufficient data to construct reliable classifiers for specific words. This limitation hindered the model's ability to generalize effectively across the entire lexicon, particularly for low-frequency or domain-specific terms. Returning to the example depicted in Figure 3 (left), we observe that the synset value \"level%1:06:00::\" is associated with only one training sentence and one test sentence. These sentences are significantly distant from each other in the NCA component space, resulting in model errors. To address this challenge, we generated sentences using ChatGPT or Gemini AI agents specifically for synset values with limited examples. These AI agents were prompted with descriptions of synsets from the WordNet dictionary. Notably, on the right side of Figure 3, we observe that this approach proved beneficial: the sentences for the synset value \"level%1:06:00::\" formed a distinct cluster, and the test sentence aligned within this cluster. To address the limitations identified in the error analysis, we propose the refined model training process outlined in Figure 4. This section delves into a detailed breakdown of each component and its contribution to the enhanced performance of the model. **Obtaining the training data. ** This project used a multi-phased approach to acquiring data, aiming to create a robust and iteratively improvable WSD model. The initial training data consisted of sentences from the widely used SemCor corpus [24]. While effective, this dataset's limitations necessitated exploring alternative sources. Figure 4: Process of the WSD training model. Here, we detail the exploration of various data augmentation techniques and their impact on the model's performance. WordNet definitions: Initially, we attempted to improve the model's performance by augmenting data using the existing WordNet synsets' definitions. However, utilizing the WSD debugging method described later, we observed a detrimental effect on accuracy when incorporating certain definitions. Consequently, some definitions were excluded during the process of iterative improvement. Artificially generated sentences: We further investigated the inclusion of artificially generated sentences based on WordNet's hypernym and hyponym relations. These sentences were constructed by combining hypernym/hyponym definitions with a phrase indicating the intended meaning. Evaluation revealed this approach to be ineffective, and the generated sentences were not used in further training. Wikipedia articles: Given the BERT neural network's pretraining on Wikipedia data, we explored the impact of incorporating additional Wikipedia sentences. This approach proved successful. Adding approximately 10 new sentences per novel synset value demonstrably improved the model's ability to identify those synsets.