A Comprehensive Survey on Word Representation Models: From Classical to State-of-the-Art Word Representation Language Models

Word representation has always been an important research area in the history of natural language processing (NLP). Understanding such complex text data is imperative, given that it rich information and can be used widely across various applications. In this survey, we explore different word models its power expression, from classical to modern-day state-of-the-art (LMS). We describe a variety methods, model designs have blossomed context NLP, including SOTA LMs. These transform large volumes into effective vector representations capturing same semantic information. Further, utilized by machine learning (ML) algorithms for NLP-related tasks. end, survey briefly discusses commonly ML- DL-based classifiers, evaluation metrics, applications these embeddings NLP