CPC G06N 3/08 (2013.01) [G06F 16/353 (2019.01); G06F 16/93 (2019.01); G06F 40/205 (2020.01); G06F 40/30 (2020.01); G06N 3/04 (2013.01); G06N 3/044 (2023.01); G06N 3/045 (2023.01)] | 20 Claims |
1. A computer-implemented method for multi-label learning and classification using one or more processors to cause steps to be performed comprising:
processing raw training texts into cleaned training texts;
parsing training labels into level-wise labels at multiple levels based on their ontological hierarchies;
training a set of two or more level-wise models of a level-wise multi-label classification model based on at least the level-wise labels and the cleaned texts, with each level-wise model related to a corresponding level of labels;
obtaining, using the trained set of two or more level-wise models, level-wise predictions from one or more inputs; and
using the level-wise predictions as inputs into a point generation model to train the point generation model to generate a reduced set of the level-wise predictions comprising a set of relevant labels.
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