# Reversal of the Word Sense Disambiguation Task Using a Deep Learning Model
## 6 Related Work  
Over the years, significant effort has been devoted to developing algorithms and systems for word sense disambiguation since its inception. Among the earliest and most intuitively comprehensible approaches is the Lesk algorithm [15]. Essentially, the Lesk algorithm counts the number of overlapping words between the target word in a sentence and the definition of the synset associated with that word. To address the limitations arising from the Lesk algorithm's heavy reliance on dictionary definitions of the synsets, several extensions have been proposed [33]. Recent advancements have focused on learning distributional word representations from large corpora and replacing the traditional bag of words' overlap with a more sophisticated cosine similarity of the vectors [34].  
In parallel, knowledge-based approaches have explored graph theory algorithms to leverage the semantic relationships provided by dictionaries. For instance, the UKB algorithm uses random walks on the semantic graph derived from WordNet to determine word senses using the PageRank metric [35].  
Alternatively, machine learning algorithms that construct classifiers from annotated text data have gained prominence. Early works in this field experimented with well-known algorithms such as decision trees [36], support vector machines [37], and neural networks [38].  
Numerous WSD systems have been developed to implement these algorithms. In our investigation, we explored systems that are accessible on GitHub. One such system, ConSeC [39], leverages deep neural networks and achieved higher accuracy than the model presented in this article. However, ConSeC requires downloading a large 5 GB model, which is significantly larger than the model discussed here (~100 MB). Additionally, the installation process can be complex (we provide a link to the Colab notebook attempting to run ConSeC in the GitHub repository).  
Another system, ESR [40], utilizes the RoBERTTa neural network and demonstrated impressive F1 metric results. However, the documentation lacks details on the implementation of the _predict_(...) function. The SparseLMMS system, which incorporates large pretrained language models, offers an online demonstration and uses a contextualized mapping mechanism [41]. However, deploying this system can be intricate, and the size of its model significantly exceeds that of the model discussed in this study. Another notable system, EWISER [42], utilizes a neural supervised architecture that taps into a wealth of knowledge by embedding information from the KB graph within the neural framework, resulting in impressive F1 metric results. Nevertheless, setting up EWISER can be a challenging process.  
In contrast, the system described in [43] leverages the BERT neural network and achieved a classification accuracy comparable with the results presented in this study. Unfortunately, the system's GitHub page lacks comprehensive documentation, posing challenges for its implementation on other NLP frameworks.  
All reviewed works and algorithms rely on the standard WordNet dictionary, and the accompanying test dataset is referenced in related works [17; 20; 21; 22; 23]. Our solution was also evaluated on this dataset. However, this work introduces a novel component of the WSD system, which we called the \"WSD debugging system\". To our knowledge, no analogous approach has been presented previously. The essence of this idea, encapsulated in the title of our article (\"Reversal WSD\"), centers on treating the existing WSD model as the gold standard. The knowledge engineer's task is to modify the WordNet dictionary in a way that maximizes the accuracy in the standard testing dataset.