Word Embeddings

Word Embeddings are dense, real-valued vectors—typically 50 to 300 dimensions—that map words into a continuous vector space, numerically capturing their semantic and syntactic relationships for machine learning models.

Word Embeddings transform raw text into the numerical format machine learning requires: dense vectors of floating-point values. This technique is fundamental to modern Natural Language Processing (NLP), allowing algorithms to understand word meaning and context. Foundational models like Google’s Word2Vec (2013) and Stanford’s GloVe learn these representations by analyzing large corpora, positioning semantically similar words close together in the vector space. This spatial relationship enables mathematical operations, such as the famous vector analogy: 'King – Man + Woman ≈ Queen,' which is critical for tasks like machine translation and sentiment analysis.

https://www.tensorflow.org/text/guide/word_embeddings