Ensemble model for entity resolution in matching problems using classification, subarea, nlp and subarea nlp machine learning models

Abstract

Systems and methods for matching entities to target objects using an ensemble model are disclosed. The ensemble model includes a general trained machine learning (ML) model (which is trained using the entirety of a training dataset) and a subarea trained ML model (which is trained using a subset of the training dataset corresponding to a specific, defined subarea) that provides potential matches to a meta-model of the ensemble model to generate a final match. The ensemble model may also include a general trained natural language processing (NLP) model and a subarea trained NLP model that provides potential matches to the meta-model. The meta-model of a quad-ensemble ML model combines the four potential matches (such as probabilities and similarities of matching specific pairs of targets objects and entities) to generate a final match (such as a final probability used to identify the final match).