We show that when strict complementarity fails to hold at a local solution, appropriate scaling of the primal-dual Lagrange multiplier estimates allows for recovering superlinear convergence in interior-point methods for Nonlinear optimization. The scaling relies on indicator sets that identify strongly active, weakly active and inactive constraints. The rate of convergence can be anywhere between 1 and 3/2 and is determined by the rate of decrease of the barrier parameter.