# Reversal of the Word Sense Disambiguation Task Using a Deep Learning Model
## 1 Introduction  
The task of determining the semantic meaning of a word from the context of a sentence is a challenging problem in natural language processing (NLP) that can be difficult for both humans and computers [1]. The word sense disambiguation (WSD) problem is one such challenge that has been the subject of extensive research in the field of NLP. In fact, some researchers believe that the WSD problem is AI-complete [2]. This means that if a computer could solve this problem, it would be able to solve all other problems of artificial intelligence at the same time. However, in this study, the WSD problem was treated as a simple data classification problem, which large neural networks must be able to solve with almost 100% accuracy if a suitable classification scheme is provided. Various WSD solutions have been proposed over several decades of research, among which, deep neural networks are the most effective [3]. These solutions model the WSD problem as a classification task, where each class corresponds to a specific concept or synset. Usually, a separate model is trained for each word on a corpus of sentences annotated with that word. However, a neural network-based WSD solution requires a large amount of annotated data, which may not be available for many words. Therefore, knowledge-based methods, which exploit the relationships between semantic categories in a knowledge base, offer an alternative way to solve the WSD problem [4; 5]. These methods infer the most likely sense of a word on the basis of the semantic context. One of the main assumptions of knowledge-based methods is that the relationships between concepts in the knowledge base are accurate and reliable, i.e., they are free of errors. To illustrate that this is not always the case, we took the word 'table' from thesemantic dictionary WordNet [5], which was the primary knowledge base used in this study, and examined its suitability as a formal ontology. The word 'table' has eight senses, six of which are nouns. Only two of these noun senses can be considered as ontology classes: \"table%1:14:00::\" (a set of data arranged in rows and columns) and \"table%1:06:01::\" (a piece of furniture having a smooth flat top that is usually supported by one or more vertical legs). The remaining four noun senses are not appropriate as ontology classes of the word \"table\". For instance, \"table%1:06:02::\" (a piece of furniture with tableware for a meal laid out on it) is a specific instance of the sense \"table%1:06:01::\" and can be ignored for many semantic analysis tasks of natural language. Another sense, \"table%1:14:01::\" (a company of people assembled at a table for a meal or game), is described in WordNet as an abstract entity and it is impossible logically derive from the WordNet knowledge base that it is a collection of physical entities. Some may contest the assertion that the word \"table\" possesses only two discernible semantic meanings, thereby raising the query whether a formal methodology can be devised for categorizing words into distinct semantic classes. This question becomes even more pertinent when considering the lack of uniformity in the existing approaches. Previous works [6; 7; 8; 9] demonstrated a significant disparity in how semantic meanings are classified for individual words. This inconsistency poses a challenge for researchers attempting to compare findings across different studies and hinders the development of a unified framework for semantic analysis. Addressing this query constitutes a focal point of this research article. The central idea of our proposed formalization revolves around harnessing the capabilities of deep learning models to extract word embedding vectors. It is assumed that these vectors, when subjected to clustering analysis, should correspond closely to the conceptual categories delineated within a formal ontology, given sufficient training and parameterization of the deep learning model.