Word embedding is the representation of a word in multi-dimensional space such that words with similar meanings have similar embedding. Each word is mapped to a vector of real numbers that represent the word. The Analytics Database function TD_WordEmbeddings produces vectors for each piece of text and finds the similarity between the texts.
The function contains training and prediction using models. The models contain each possible token and its corresponding vectors. The closer the distance between the vectors the more the similarity. Function operations are token-embedding, doc-embedding, token2token-similarity, and doc2doc-similarity.