To preserve DIH, we propose a novel word embedding method, distributional inclusion vector embedding (DIVE), which fixes the two flaws by performing non-negative factorization (NMF) (Lee and Seung, 2001)  where k I is a constant which shifts PMI value like SGNS, Z = |D| |V | is the average word frequency, and |V | is the vocabulary size.
a combination of unsupervised DIVE with the proposed scoring functions produces new state-of-the-art performances on many datasets in the unsupervised regime.