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：US11003861B2

公开(公告)日：2021-05-11

申请号：US16275025

申请日：2019-02-13

Applicant: SAP SE

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

IPC: G06F40/00 ,  G06F40/30 ,  G06F40/211 ,  G06F40/284

Abstract: Various examples are directed to systems and methods for classifying text. A computing device may access, from a database, an input sample comprising a first set of ordered words. The computing device may generate a first language model feature vector for the input sample using a word level language model and a second language model feature vector for the input sample using a partial word level language model. The computing device may generate a descriptor of the input sample using a target model, the input sample, the first language model feature vector, and the second language model feature vector and write the descriptor of the input sample to the database.

公开(公告)号：US10824808B2

公开(公告)日：2020-11-03

申请号：US16196153

申请日：2018-11-20

Applicant: SAP SE

Inventor： Christian Reisswig ,  Eduardo Vellasques ,  Sohyeong Kim ,  Darko Velkoski ,  Hung Tu Dinh

IPC: G10L15/02 ,  G06F40/295 ,  G06F40/289 ,  G06N3/04 ,  G06N3/08 ,  G06F40/30

Abstract: Disclosed herein are system, method, and computer program product embodiments for robust key value extraction. In an embodiment, one or more hierarchical concepts units (HCUs) may be configured to extract key value and hierarchical information from text inputs. The HCUs may use a convolutional neural network, a recurrent neural network, and feature selectors to analyze the text inputs using machine learning techniques to extract the key value and hierarchical information. Multiple HCUs may be used together and configured to identify different categories of hierarchical information. While multiple HCUs may be used, each may use a skip connection to transmit extracted information to a feature concatenation layer. This allows an HCU to directly send a concept that has been identified as important to the feature concatenation layer and bypass other HCUs.

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.

公开(公告)号：US20200257764A1

公开(公告)日：2020-08-13

申请号：US16275025

申请日：2019-02-13

Applicant: SAP SE

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

IPC: G06F17/27

Abstract: Various examples are directed to systems and methods for classifying text. A computing device may access, from a database, an input sample comprising a first set of ordered words. The computing device may generate a first language model feature vector for the input sample using a word level language model and a second language model feature vector for the input sample using a partial word level language model. The computing device may generate a descriptor of the input sample using a target model, the input sample, the first language model feature vector, and the second language model feature vector and write the descriptor of the input sample to the database.