Word Embeddings

Word embeddings are a series of language modeling and feature learning techniques in natural language processing that map words or phrases from a vocabulary to a real-valued vector space, achieving a numerical representation of semantic and syntactic relationships. The goal is to capture the contextual associations and latent structures among words, thereby enhancing the performance and accuracy of machine learning models in tasks such as text classification and sentiment analysis. The application value of word embeddings lies in their ability to effectively address the sparsity and high dimensionality issues of traditional word representations, providing strong support for complex language tasks.