After contextualization, we apply singular value decomposition (SVD) for dimensionality reduction to reduce the number of model parameters, keeping the k most significant singular values and vectors.
we apply SVD to the m-byn term-context matrix M produced by the SIM-PLE method, resulting in the truncated matrices where U k is a m-by-k matrix, Σ k is k-by-k, and V k is n-by-k.