Comparative study of different word embedding learning techniques

Abstract

Natural language processing (NLP) has been transformed by word embedding, which makes it possible for sophisticated language models to comprehend and produce text that is similar to that of humans. Word embedding techniques have become essential tools for many natural language processing (NLP) tasks, such as information retrieval, machine translation, and sentiment analysis, because they can capture the syntactic and semantic relationships between words. We go deeply into the field of word embedding in this research article with the goal of offering an exhaustive examination of its foundational ideas, approaches, and uses.