A word embedding is a dense numerical vector representation of a word used in natural language processing. It encodes semantic relationships so that words with similar meanings or usage patterns tend to have similar positions in vector space. Word embeddings support tasks such as language modeling, search, classification, and text generation.
A representation of a word in natural language processing. Typically, the representation is a real-valued vector that encodes the meaning of the word in such a way that words that are closer in the vector space are expected to be similar in meaning.
Representing each word in a word set within an embedding vector; that is, representing each word as a vector of floating-point values between 0.0 and 1.0. Words with similar meanings have more-similar representations than words with different meanings. For example, carrots , celery , and cucumbers would all have relatively similar representations, which would be very different from the representations of airplane , sunglasses , and toothpaste.
Word embeddings are a way to represent words as dense vectors of floating-point values. This allows similar words to have similar encodings. Word embeddings are often used in generative AI to capture the relationships between words and generate new text or code, no sources. In generative AI, word embeddings can be used to train models that can generate new text or code. By understanding the relationships between words, generative AI models can create new content that is both coherent and relevant.