US 12,346,656 B2
Feature crossing for machine learning
Qingyi Lu, Los Angeles, CA (US); Yuan Gao, Los Angeles, CA (US); Hongyu Xiong, Los Angeles, CA (US); Han Wang, Los Angeles, CA (US); Bin Liu, Los Angeles, CA (US); Xiangyu Zeng, Los Angeles, CA (US); Rui Li, Los Angeles, CA (US); and Yiqi Feng, Los Angeles, CA (US)
Assigned to Lemon Inc., Grand Cayman (KY)
Filed by Lemon Inc., Grand Cayman (KY)
Filed on May 5, 2022, as Appl. No. 17/737,918.
Prior Publication US 2023/0359824 A1, Nov. 9, 2023
Int. Cl. G06F 17/00 (2019.01); G06F 16/901 (2019.01); G06F 40/295 (2020.01); G06F 40/30 (2020.01); G06N 20/00 (2019.01)
CPC G06F 40/295 (2020.01) [G06F 16/9024 (2019.01); G06F 40/30 (2020.01); G06N 20/00 (2019.01)] 20 Claims
OG exemplary drawing
 
9. A system, comprising:
at least one processor; and
at least one memory communicatively coupled to the at least one processor and comprising computer-readable instructions that upon execution by the at least one processor cause the at least one processor to perform acts comprising:
determining a semantic correlation relationship between a plurality of feature categories, the semantic correlation relationship indicating respective degrees of semantic correlation between respective pairs of feature categories among the plurality of feature categories, wherein the determining the semantic correlation relationship comprises:
identifying a set of named entities from a corpus, the corpus comprising a plurality of text sequences,
classifying the set of named entities into a plurality of entity clusters, each of the plurality of entity clusters being associated with one of the plurality of feature categories, and
determining the respective degrees of semantic correlation among the plurality of feature categories based on relative positioning of the plurality of entity clusters within the plurality of text sequences;
obtaining at least two features classified in at least two of the plurality of feature categories for machine learning; and
performing feature crossing on the at least two features based on the semantic correlation relationship, wherein the performing the feature crossing comprises:
determining a target degree of semantic correlation between the at least two feature categories based on the semantic correlation relationship,
in accordance with a determination that the target degree of semantic correlation exceeds a threshold degree of semantic correlation, applying a feature crossing operation on the at least two features to generate a crossed feature, and
in accordance with a determination that the target degree of semantic correlation is below the threshold degree of semantic correlation, ceasing to apply the feature crossing operation on the at least two features.