CROSS-LINGUAL TEXT CLASSIFICATION USING CHARACTER EMBEDDED DATA STRUCTURES

摘要

A device may be configured to obtain text from a document. The device may perform embedding to obtain a data structure indicating probabilities associated with characters included in the text and apply a first convolution to the data structure to obtain different representations of the characters included in the text. In addition, the device may apply parallel convolution to the different representations to obtain multiple sets of character representations, subsample the multiple sets of character representations, and pool the subsampled multiple sets of character representations into a merged data structure. The device may provide the merged data structure to a fully connected layer, of a convolutional neural network, to produce data representing features of the text; and provide the data representing features of the text to an inference layer, of the convolutional neural network, that provides data indicating a classification for the text.