On the one hand, it uses SGNS window sampling, negative sampling, and stochastic gradient descent (SGD) to minimize a loss function that weights frequent co-occurrences heavily but also takes into account negative co-occurrence.
since PPMI generally outperforms PMI on semantic similarity tasks (Bullinaria and Levy, 2007), rather than implicitly factorize a shifted PMI matrix (like SGNS), LexVec explicitly factorizes the PPMI matrix.