Systems and methods for training a multi-label classification model

Abstract

A method, system and computer-readable medium for training a multi-label classification model to learn a new entity added to a dictionary of the multi-label classification model are disclosed. The method includes: identifying candidate training instances based on the new entity; generating a training record for each of the candidate training instances, where the training record includes the candidate training instance and the new entity as a new label; for each training record: adding existing entities from the dictionary, such that the training record includes a set of labels including the new label; and refining the set of labels to remove labels that are not related to the corresponding training instance. The method further includes training the multi-label classification model using the refined training records.

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