Introduction Word embeddings are numerical representations of words in a continuous vector space, by learning the distribution of words in text. They capture both semantic and syntactic relationships between words, making them suitable for various downstream Natural Language Processing (NLP) tasks. Typically, words with similar meanings are mapped to nearby points in the vector spaces.…