Similarly to the global weighting, we assigned each occurrence of a term to its local weighting bin l, but this time by simply capping tf at the total number of local weighting bins |L|: ) Let's note that this particular representation does not really need rounding since tf is already a positive integer.
in a more general case, tf can be normalized by document length (as is done in BM25 and language models) and thus local weighting would become a continuous function.