Abstract: Technologies are disclosed herein for generating and utilizing machine-learning generated classifiers configured to identify document relationships. Manually-generated data is captured that indicates if documents in a document corpus have a relationship with one another, such as duplicates or variations. A determination may then be made as to whether a classifier is to be generated based on the duplicate decision data. If a classifier is to be generated, machine learning may be performed using training documents from the document corpus and the duplicate decision data to generate a classifier. The machine-learning generated classifier may then be utilized in a production environment to determine whether a new document is a duplicate of documents in the document corpus and/or to identify other relationships between documents in the document corpus, such as documents that are similar or are variations of one another.

Abstract: Embodiments of a system and method for identifying structured data items lacking requisite information for rule-based duplicate detection are described. Embodiments may include generating a deficiency score for each of multiple structured data items including applying a set of rules based on duplicate detection techniques to each given structured data item in order to perform a comparison of the given structured data item to itself. The deficiency score of the given structured data item may be based on a result of the comparison. Embodiments may also include, based on the deficiency scores of the structured data items, identifying one or more deficient structured data items having less than a requisite quantity of information for performing duplicate detection on structured data items. Embodiments may also include identifying one or more key attributes missing from some of the one or more deficient structured data items and requesting those key attributes.