| US 11,797,705 B1 | ||
| Generative adversarial network for named entity recognition | ||
| Daniel Voinea, Iasi (RO); Aurelian Tutuianu, Iasi (RO); Silviu Catalin Poede, Iasi (RO); Marian-Razvan Udrea, Iasi (RO); and Brent Gregory, Sammamish, WA (US) | ||
| Assigned to Amazon Technologies, Inc., Seattle, WA (US) | ||
| Filed by Amazon Technologies, Inc., Seattle, WA (US) | ||
| Filed on Dec. 11, 2019, as Appl. No. 16/711,260. | ||
| Int. Cl. G06F 21/62 (2013.01); G06F 40/295 (2020.01); G06F 18/24 (2023.01); G06N 3/045 (2023.01) | ||
| CPC G06F 21/6245 (2013.01) [G06F 18/24 (2023.01); G06F 40/295 (2020.01); G06N 3/045 (2023.01)] | 20 Claims |
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1. A system, comprising:
a generator of a generative adversarial network (GAN) implemented by one or more processors and memory and configured to:
be trained to generate one or more synthetic data based on training sets to the generator, the one or more synthetic data including information that simulates sensitive information; and
a discriminator of the GAN implemented by the one or more processors and memory and configured to:
be trained to classify each one of training sets to the discriminator that include the one or more synthetic data generated by the generator and one or more positive examples as synthetic data or a positive example, the one or more positive examples each including the sensitive information; and
be deployed to identify that a data store includes at least some data matching a named entity type based on samples of data of the data store, wherein the samples are sampled according to a distribution of the data within the data store, the named entity type representing the sensitive information.
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