  This is an experiential study of investigating a consistent method for
deriving the correlation between sentence vector and semantic meaning of a
sentence. We first used three state-of-the-art word/sentence embedding methods
including GPT-3, Word2Vec, and Sentence-BERT, to embed plain text sentence
strings into high dimensional spaces. Then we compute the pairwise distance
between any possible combination of two sentence vectors in an embedding space
and map them into a matrix. Based on each distance matrix, we compute the
correlation of distances of a sentence vector with respect to the other
sentence vectors in an embedding space. Then we compute the correlation of each
pair of the distance matrices. We observed correlations of the same sentence in
different embedding spaces and correlations of different sentences in the same
embedding space. These observations are consistent with our hypothesis and take
us to the next stage.
