CPC G06F 16/3347 (2019.01) [G06F 16/245 (2019.01); G06F 16/248 (2019.01); G06F 18/2413 (2023.01); G06N 3/04 (2013.01); G06N 3/045 (2023.01); G06N 5/02 (2013.01); G06V 20/62 (2022.01); G06F 17/16 (2013.01)] | 20 Claims |
1. One or more computing devices for processing an instance of text, comprising:
hardware logic circuitry, the hardware logic circuitry including: (a) one or more hardware processors that perform operations by executing machine-readable instructions stored in a memory, and/or (b) one or more other hardware logic units that perform operations using a task-specific collection of logic gates, the operations including:
receiving an instance of input text in response to an action taken using a user computing device;
generating an input term-frequency (TF) vector that includes frequency information relating to frequency of occurrence of terms in the input text, the input TF vector corresponding to a vector having a prescribed dimensionality and having a plurality of TF values, each dimension of the input TF vector corresponding to a term in a vocabulary of predetermined size;
using a TF-modifying neural network to modify the frequency information in the input TF vector, associated with respective terms in the vocabulary, by respective machine-trained weighting factors associated with respective dimensions of the input TF vector, to produce an intermediate vector having a same dimensionality as the input TF vector, the TF-modifying neural network operating by modifying each particular TF value in the input TF vector by a corresponding machine-trained weighting factor that is specifically associated with the particular TF value, the TF-modifying neural network being implemented by the hardware logic circuitry and including at least one layer of neurons;
using a projection neural network to project the intermediate vector into an embedding vector having a dimensionality that is less than the dimensionality of the input TF vector, the embedding vector providing a distributed compact representation of semantic information in the input text, the projection neural network being implemented by the hardware logic circuitry and including at least one layer of neurons; and
utilizing the embedding vector to produce an output result that is accessible by the user computing device.
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