WORD EMBEDDING MODELS WITH MACHINE LEARNING BASED CONTEXT DEPEND AND CONTEXT INDEPENDENT TECHNIQUES

Abstract

Word embedding techniques play a crucial role in natural language processing by enabling machines to represent textual data in a numerical form that preserves semantic and syntactic information. Over time, embedding models have evolved from traditional context-independent approaches to advanced context-dependent representations driven by deep learning. This review presents a comprehensive overview of machine learning–based word embedding models, focusing on both static and dynamic techniques. Context-independent models such as Word2Vec, GloVe, and FastText generate fixed vector representations for words based on global or local co-occurrence statistics, offering computational efficiency but limited contextual understanding. In contrast, context-dependent models including ELMo, BERT, and transformer-based architectures produce dynamic embeddings that adapt to surrounding linguistic context, effectively addressing issues such as polysemy and semantic ambiguity. The paper analyzes the working principles, strengths, and limitations of these embedding approaches and highlights their impact on downstream NLP tasks. This review aims to provide researchers and practitioners with a clear understanding of the progression of word embedding methodologies and their significance in modern language modeling applications.