公开(公告)号：US20230086145A1

公开(公告)日：2023-03-23

申请号：US17936761

申请日：2022-09-29

Applicant: Beijing Baidu Netcom Science Technology Co., Ltd.

Inventor： Wenbin JIANG ,  Yajuan LV ,  Yong ZHU ,  Hua WU ,  Haifeng WANG

IPC: G06F16/738 ,  G06N5/02

Abstract: A method of processing data, a device, and a medium are provided, which relate to a field of an artificial intelligence technology, in particular to fields of computer vision, natural language technology, speech technology, deep learning and knowledge graph. The method of processing data includes: generating a video feature, a question feature and an answer feature based on acquired video data, acquired question data and acquired candidate answer data; determining a link relationship between the video feature, the question feature and the answer feature; and determining a matching result for the video data, the question data and the candidate answer data based on the link relationship.

公开(公告)号：US20210192364A1

公开(公告)日：2021-06-24

申请号：US17124030

申请日：2020-12-16

Applicant: BEIJING BAIDU NETCOM SCIENCE TECHNOLOGY CO., LTD.

Inventor： Haifeng WANG ,  Wenbin JIANG ,  Yajuan LV ,  Yong ZHU ,  Hua WU

IPC: G06N5/02 ,  G06F40/30 ,  G06F40/279 ,  G06K9/62

Abstract: The present application discloses a text processing method and device based on natural language processing and a knowledge graph, and relates to the in-depth field of artificial intelligence technology. A specific implementation is: an electronic device uses a joint learning model to obtain a semantic representation, which is obtained by the joint learning model by combining knowledge graph representation learning and natural language representation learning, it combines a knowledge graph representation learning and a natural language representation learning, compared to using only the knowledge graph representation learning or the natural language representation learning to learn semantic representation of a prediction object, factors considered by the joint learning model are more in quantity and comprehensiveness, so accuracy of semantic representation can be improved, and thus accuracy of text processing can be improved.

公开(公告)号：US20240338862A1

公开(公告)日：2024-10-10

申请号：US18749461

申请日：2024-06-20

Applicant: BEIJING BAIDU NETCOM SCIENCE TECHNOLOGY CO., LTD.

Inventor： Jiachen LIU ,  Xinyan XIAO ,  Hua WU ,  Guohao LI ,  Wei LI ,  Hong ZHU ,  Qiaoqiao SHE ,  Yajuan LV

IPC: G06T11/00 ,  G06F40/35

CPC classification number: G06T11/00 ,  G06F40/35

Abstract: A method is provided that includes: obtaining current dialogue data; determining a requirement type of the user in the current round of dialogue based on the current dialogue data; in response to the requirement type being an image processing requirement, determining an action sequence for implementing the image processing requirement; executing the action sequence to generate a target image; and generating response data corresponding to the user input data based on the target image.

公开(公告)号：US20230342667A1

公开(公告)日：2023-10-26

申请号：US18179266

申请日：2023-03-06

Applicant: BEIJING BAIDU NETCOM SCIENCE TECHNOLOGY CO., LTD.

Inventor： Zenan LIN ,  Huapeng QIN ,  Min ZHAO ,  Guoxin ZHANG ,  Yajuan LV

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: A semantic classification model training method includes that a sample query template and a label category of at least one category to be predicted in the sample query template are acquired, where the sample query template is constructed according to a sample query statement and a number of the at least one category to be predicted; the sample query template is input to the pre-constructed semantic classification model to obtain a sample semantic category of the at least one category to be predicted; and the semantic classification model is trained according to the sample semantic category and the label category of the at least one category to be predicted.

公开(公告)号：US20230153337A1

公开(公告)日：2023-05-18

申请号：US18157452

申请日：2023-01-20

Applicant: Beijing Baidu Netcom Science Technology Co., Ltd.

Inventor： Wenbin JIANG ,  Yajuan LV ,  Chunguang CHAI ,  Yong ZHU

IPC: G06F16/332 ,  G06F40/30

CPC classification number: G06F16/3329 ,  G06F40/30

Abstract: A question answering method, a method of training a question answering model, a device, and a medium are provided, which relate to a field of artificial intelligence technology, in particular to fields of natural language processing technology, deep learning technology, and knowledge mapping technology. The question answering method includes: obtaining data to be processed, wherein the data to be processed includes a question and candidate answers; performing general semantic understanding on the data to be processed to obtain a general data feature; selecting a target question answering mode from candidate question answering modes based on the general data feature; and processing the general data feature by using the target question answering mode, to obtain a target answer for the question from the candidate answers.