Learning Word Embeddings

Learning word embeddings is a key technology in natural language processing, aiming to map words into high-dimensional vector spaces to capture semantic and syntactic relationships between them. Its core objective is to reflect the similarity and relevance of words in context through mathematical representations, thereby enhancing the machine's ability to understand language. The application value of word embeddings is extensive, including but not limited to sentiment analysis, machine translation, text classification, etc., which can significantly improve the performance and robustness of models.