公开(公告)号：US11170167B2

公开(公告)日：2021-11-09

申请号：US16364777

申请日：2019-03-26

申请人： TENCENT AMERICA LLC

发明人： Kun Xu ,  Chao Weng ,  Chengzhu Yu ,  Dong Yu

IPC分类号： G06F40/274 ,  G06F40/30 ,  G06F40/232 ,  G06F40/284

摘要： Method and apparatus for automatically predicting lexical sememes using a lexical dictionary, comprising inputting a word, retrieving the word's semantic definition and sememes corresponding to the word from an online dictionary, setting each of the retrieved sememes as a candidate sememe, inputting the word's semantic definition and candidate sememe, and estimating the probability that the candidate sememe can be inferred from the word's semantic definition.

公开(公告)号：US11610060B2

公开(公告)日：2023-03-21

申请号：US17469540

申请日：2021-09-08

申请人： TENCENT AMERICA LLC

发明人： Kun Xu ,  Chao Weng ,  Chengzhu Yu ,  Dong Yu

IPC分类号： G06F40/274 ,  G06F40/30 ,  G06F40/232 ,  G06F40/284

摘要： Method and apparatus for automatically predicting lexical sememes using a lexical dictionary, comprising inputting a word, retrieving the word's semantic definition and sememes corresponding to the word from an online dictionary, setting each of the retrieved sememes as a candidate sememe, inputting the word's semantic definition and candidate sememe, and estimating the probability that the candidate sememe can be inferred from the word's semantic definition.

公开(公告)号：US20230196087A1

公开(公告)日：2023-06-22

申请号：US17510782

申请日：2021-10-26

申请人： TENCENT AMERICA LLC

发明人： Lifeng Jin ,  Linfeng Song ,  Kun Xu ,  Dong Yu

IPC分类号： G06N3/08 ,  G06N3/04 ,  G06K9/62

CPC分类号： G06N3/08 ,  G06K9/6256 ,  G06N3/0454

摘要： There is included a method and apparatus comprising computer code for a joint training method using neural networks with noise-robust losses comprising encoding input tokens from a noisy dataset into input vectors using an input encoder; predicting a label based on the input vectors using a classifier model; calculating a beta value based on the input vectors and the label using a label quality predictor model, wherein the beta value is instance-specific for each training instance; and j oint training more than one model using a first modified loss function based on the beta value and an entropy value.