[DoctorOetker]

I just discovered https://github.com/shahzainmehboob/word2matrices from about 5 years ago. There doesn't seem to be an associated paper though, and its not immediately clear what norm they used, but it looks like they flatten the matrix and compute the inner product on the resulting vector, by analogy with word2vec vectors embeddings that occur frequently should thus be closer to the 0 matrix. i.e. they seem to be using the Frobenius norm of matrices, which seems very reasonable.

[DoctorOetker]

They also only optimize for bigram statistics (2 matrices), so they don't utilize the associative property of matrices A(BC)=(AB)C, corresponding to string concatenation...