公开(公告)号：US20200159828A1

公开(公告)日：2020-05-21

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: G06F17/27 ,  G06N3/08 ,  G06N3/04

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：US20230075369A1

公开(公告)日：2023-03-09

申请号：US17469075

申请日：2021-09-08

Applicant: SAP SE

Inventor： Sohyeong KIM ,  Christian REISSWIG

IPC: G06N20/20 ,  G06K9/62

Abstract: Systems and methods include training of each of a plurality of models based on a first set of training data comprising a first plurality of pairs, each of the first plurality of pairs comprising a feature and a corresponding label, inputting of each of a plurality of features into each of the plurality of trained models to generate, for each feature of the plurality of features, a plurality of output labels, determining, for each of the plurality of features, a pseudo-label based on the plurality of output labels generated for the feature, determining a second set of training data comprising a second plurality of pairs, each of the second plurality of pairs comprising one of the plurality of features and a pseudo-label determined for the one of the plurality of features, and training an inference model to output an inferred label based on the first set of training data and the second set of training data.