Word2Vec Model

Word2Vec is a foundational model for generating word embeddings, representing words as dense vectors capturing semantic relationships learned from large text corpora. Current research explores its applications beyond text, including adapting it for audio and image data, and investigates improvements to its efficiency and accuracy, such as exploring alternative optimization strategies and examining the impact of different initialization methods for expanding vocabulary. These advancements are driving progress in various fields, including natural language processing, cybersecurity (e.g., vulnerability detection), and materials science (e.g., searching for chromate replacements), by enabling more effective analysis of complex data.