BOOTSTRAPPING MULTIPLE VARIETIES OF GROUND TRUTH FOR A COGNITIVE SYSTEM

摘要

Curating high-quality ground truth is an important but difficult part of training a cognitive system. The invention greatly simplifies this process by determining the value that particular training data has in improving existing ground truth. Candidate training data of different types (text, audio, images) is extracted from an interaction log, and each entry is analyzed to arrive at a training value score. The analysis generates multiple component scores which are combined for the final score. The component scores may include a per-feature variability score, a cross-feature variability score, and an accuracy score. A set of the unverified entries may be presented to a user based on the training value scores, and the user can select which of the entries in the set should be included as new ground truths. The ground truths can then be updated by adding the selected entries.