 "n"-gram models are often criticized because they lack any explicit representation of long range dependency.This is because the only explicit dependency range is ("n" − 1) tokens for an "n"-gram model, and since natural languages incorporate many cases of unbounded dependencies (such as wh-movement), this means that an "n"-gram model cannot in principle distinguish unbounded dependencies from noise (since long range correlations drop exponentially with distance for any Markov model).For this reason, "n"-gram models have not made much impact on linguistic theory, where part of the explicit goal is to model such dependencies.