Patent ID: 11875253
Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 14:
15. A computer-implemented method, the method comprising:
processing input data via a randomly initialized entity resolution model, wherein the input data comprise unlabeled input data;
identifying a section of the unlabeled input data to be used in training the randomly initialized entity resolution model, wherein said identifying comprises implementing one or more active learning algorithms in connection with the randomly initialized entity resolution model, and wherein implementing the one or more active learning algorithms comprises partitioning the unlabeled input data into two or more subsets using at least one partition sampling mechanism, and selecting, as the section of the unlabeled input data to be used in training the randomly initialized entity resolution model, one or more portions of the unlabeled input data from each of the two or more subsets based at least in part on entropy values assigned to the unlabeled input data;
training, using (i) the section of the unlabeled input data and (ii) one or more deep learning techniques, the randomly initialized entity resolution model, wherein using the one or more deep learning techniques comprise implementing one or more distributed representations of entity record pairs for classification; and
performing one or more entity resolution tasks by applying the trained randomly initialized entity resolution model to one or more datasets;
wherein the method is carried out by at least one computing device.