A Review: Word Embedding Models with Machine Learning Based Context Depend and Context Independent Techniques

Abstract

Natural language processing (NLP) has been transformed by word embedding models, which convert text into meaningful numerical representations. These models fall into two general categories: context-dependent methods like ELMo, BERT, and GPT, and context-independent methods like Word2Vec, GloVe, and FastText. Although static word representations are provided by context-independent models, polysemy and contextual subtleties are difficult for them to capture. These issues are addressed by context-dependent approaches that make use of sophisticated deep learning architectures to produce dynamic embeddings that are impacted by the surrounding text. This review highlights the machine learning underpinnings of these paradigms while examining their development, approaches, and comparative advantages. We examine their benchmarks, applications, and the trade-offs associated with various use cases. The study also identifies future research directions, such as hybrid embeddings and multimodal learning, and highlights contemporary issues, such as scalability and interpretability. The goal of this thorough review is to assist practitioners and researchers in choosing and refining embedding strategies for a variety of NLP tasks.