A drift(t, n, m) of 0.2 corresponds to a 20% difference in average similarity between L 1...n and L (N −m)...N for term t. Drift can be used as a post-processing step to filter terms that are a possible consequence of drift.
our main proposal is to incorporate the drift measure directly within the WMEB bootstrapping algorithm, to detect and then prevent drift occuring.