# Reversal of the Word Sense Disambiguation Task Using a Deep Learning Model
## 2 The WSD Framework  
The proposed word sense disambiguation framework sits within a broader natural language processing pipeline (Figure 2). This minimal representation showcases the system's integration and clarifies its role in addressing the WSD problem. The framework comprises eight key components. To ensure reproducibility, we have developed a comprehensive Google Colab notebook that mirrors the step-by-step process depicted in Figure 2. This Colab notebook serves as a practical guide for researchers and practitioners interested in implementing the proposed method. Readers can access the Colab notebook via the GitHub repository associated with this project (Supplementary Materials). The Colab notebook is designed for simplicity and ease of use. By executing a single command (\"Run All\"), users can seamlessly reproduce our entire methodology. In the following description, we provide a detailed breakdown of each of the eight essential steps involved in this word sense disambiguation framework:  
1. Your text: The initial stage begins by receiving raw text input. 2. spaCy NLP: This component processes the text, extracting word lemmas and determining grammatical forms through spaCy's NLP models [11]. 3. KerasNLP BERT: Leveraging the KerasNLP library [12], the system generates contextual BERT neural network embedding vectors for each word in the sentence. 4. Word alignment: This alignment results from a combination of both the spaCy and KerasNLP models, creating an index that maps corresponding words between the two representations. 5. NLTK WordNet: Using the NLTK [13] WordNet synset tags, this module annotates all the words in the text. The algorithm selects the most frequent synset for each word. 6. NCA projections: The system projects each word into an n-dimensional space derived from the training data, capturing the essential semantic relationships. 7. kNN classification: The kNN method is used for the classification of words that potentially have multiple semantic meanings, where their lemma can be described by several WordNet synset values. 8. Final classification: This model consolidates the information from previous stages, providing the final, refined classification for each word. Detailed descriptions of the critical components follow, offering a deeper understanding of the WSD framework's inner workings. Figure 2: A general implementation of the WSD method. spaCy NLP. ** The core component of the proposed WSD framework is the spaCy NLP library. spaCy offers several functionalities that are critical to WSD, each addressing a specific aspect of the problem, as follows. * **Tokenization and lemmatization. ** spaCy segments the input text into individual words (tokens) and identifies the lemma (base form) for each token. This is crucial for WSD, as the synset classifier operates on lemmas, ensuring consistent representation across different inflections of the word. * **Noun phrase chunking. ** spaCy identifies noun phrases (NPs) within the text. NPs play a twofold role. First, they help pinpoint potential word combinations aligning with distinct synsets in WordNet. Second, chunking facilitates the identification of the head noun within the NP, enabling the selection of the most relevant synset for the entire phrase. * **Part-of-speech (POS) tagging. ** spaCy assigns grammatical tags (POS tags) to each word, indicating its role within the sentence (e.g., noun, verb, adjective). POS tags are valuable features for the synset classifier, providing additional context alongside the lemma for more accurate WSD. * **Dependency parsing. ** spaCy can also extract the dependency relationships between words in a sentence. While not currently utilized in this WSD framework, dependency information holds promise for future development. Analyzing these relationships could refine word sense disambiguation by considering the syntactic structure of the sentence. * **Named entity recognition (NER):** NLP libraries often include NER modules that identify predefined named entities such as people, locations, and organizations.