公开(公告)号：US20200258498A1

公开(公告)日：2020-08-13

申请号：US16270328

申请日：2019-02-07

Applicant: SAP SE

Inventor： Christian Reisswig ,  Darko Velkoski ,  Sohyeong Kim ,  Hung Tu Dinh ,  Faisal El Hussein

IPC: G10L15/06 ,  G10L15/22 ,  G10L15/183

Abstract: Various examples described herein are directed to systems and methods for analyzing text. A computing device may train an autoencoder language model using a plurality of language model training samples. The autoencoder language mode may comprise a first convolutional layer. Also, a first language model training sample of the plurality of language model training samples may comprise a first set of ordered strings comprising a masked string, a first string preceding the masked string in the first set of ordered strings, and a second string after the masked string in the first set of ordered strings. The computing device may generate a first feature vector using an input sample and the autoencoder language model. The computing device may also generate a descriptor of the input sample using a target model, the input sample, and the first feature vector.

公开(公告)号：US10963645B2

公开(公告)日：2021-03-30

申请号：US16270328

申请日：2019-02-07

Applicant: SAP SE

Inventor： Christian Reisswig ,  Darko Velkoski ,  Sohyeong Kim ,  Hung Tu Dinh ,  Faisal El Hussein

IPC: G06F40/30 ,  G10L15/183

Abstract: Various examples described herein are directed to systems and methods for analyzing text. A computing device may train an autoencoder language model using a plurality of language model training samples. The autoencoder language mode may comprise a first convolutional layer. Also, a first language model training sample of the plurality of language model training samples may comprise a first set of ordered strings comprising a masked string, a first string preceding the masked string in the first set of ordered strings, and a second string after the masked string in the first set of ordered strings. The computing device may generate a first feature vector using an input sample and the autoencoder language model. The computing device may also generate a descriptor of the input sample using a target model, the input sample, and the first feature vector.