US 11,748,613 B2
Systems and methods for large scale semantic indexing with deep level-wise extreme multi-label learning
Dingcheng Li, Sammamish, WA (US); Jingyuan Zhang, San Jose, CA (US); and Ping Li, Bellevue, WA (US)
Assigned to Baidu USA LLC, Sunnyvale, CA (US)
Filed by Baidu USA, LLC, Sunnyvale, CA (US)
Filed on May 10, 2019, as Appl. No. 16/409,148.
Prior Publication US 2020/0356851 A1, Nov. 12, 2020
Int. Cl. G06F 16/93 (2019.01); G06F 16/35 (2019.01); G06F 40/205 (2020.01); G06F 40/30 (2020.01); G06N 3/08 (2023.01); G06N 3/04 (2023.01); G06N 3/044 (2023.01); G06N 3/045 (2023.01)
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
OG exemplary drawing
 
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.