Dynamic detection of cross-document associations

An apparatus for generating a subset of related document objects for a predictive entity, the apparatus comprising at least one processor and at least one non-transitory memory including program code, the at least one non-transitory memory and the program code configured to, with the at least one processor, cause the apparatus to at least:

• identify a set of entity metadata features associated with the predictive entity;
• generate, using an entity-document correlation machine learning model and based at least in part on the set of entity metadata features, the set of related document objects for the predictive entity, wherein (i) the entity-document correlation machine learning model is generated at least in part using a set of inferred annotative relationships between a set of input index documents and a set of inferred reference documents, (ii) each inferred annotative relationship of the set of inferred annotative relationships is associated with a corresponding input index document of the set of input index documents, and (iii) each inferred annotative relationship of the set of inferred annotative relationships describes a related reference document subset of the set of inferred reference documents for the corresponding input index document that is associated with the inferred annotative relationship;
• and
• initiate the performance of one or more prediction-based actions based at least in part on the set of related document objects.

A non-transitory computer storage medium comprising instructions for generating a subset of related document objects for a predictive entity, the instructions being configured to cause one or more processors to at least perform operations configured to:

• identify a set of entity metadata features associated with the predictive entity;
• generate, using an entity-document correlation machine learning model and based at least in part on the set of entity metadata features, the set of related document objects for the predictive entity, wherein (i) the entity-document correlation machine learning model is generated at least in part using a set of inferred annotative relationships between a set of input index documents and a set of inferred reference documents, (ii) each inferred annotative relationship of the set of inferred annotative relationships is associated with a corresponding input index document of the set of input index documents, and (iii) each inferred annotative relationship of the set of inferred annotative relationships describes a related reference document subset of the set of inferred reference documents for the corresponding input index document that is associated with the inferred annotative relationship;
• and
• initiate the performance of one or more prediction-based actions based at least in part on the set of related document objects.