1. A computer-implemented method, comprising: based on reference data that includes pairs of item description entries and labels that each indicate whether a given pair of item description entries are duplicates, generating a first machine learning model for determining whether a given pair of information items is a duplicate pair; wherein a given duplicate pair represents different item description entries that describe a common item; identifying one or more false positive pairs, wherein a given false positive pair is a pair of information items that the first machine learning model indicates as duplicates and which are labeled within the reference data as non-duplicates; selecting one or more of the identified false positive pairs as candidates to be corrected within the reference data; and subsequent to a correction of the reference data including at least one label of a selected false positive pair being modified to indicate that pair is a duplicate pair, generating based on the corrected reference data a new machine learning model for determining whether pairs of information items are duplicates.