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：US20230018489A1

公开(公告)日：2023-01-19

申请号：US17862519

申请日：2022-07-12

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

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

IPC: G06F16/242 ,  G06F16/21 ,  G06F16/245 ,  G06N5/02

Abstract: The present disclosure discloses a method for acquiring a structured question-answering (QA) model, a QA method and corresponding apparatuses, and relates to knowledge graph and deep learning technologies in the field of artificial intelligence technologies. A specific implementation solution involves: acquiring training samples corresponding to N structured QA database types, the training samples including question samples, information of the structured QA database types and query instruction samples used by the question samples to query structured QA databases of the types, N being an integer greater than 1; and training a text generation model by using the training samples to obtain the structured QA model, wherein the question samples and the information of the structured QA database types are taken as input to the text generation model, and the query instruction samples are taken as target output of the text generation model.

公开(公告)号：US20210263974A1

公开(公告)日：2021-08-26

申请号：US17173318

申请日：2021-02-11

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

Inventor： Qian LI ,  Yabing SHI ,  Ye JIANG ,  Chunguang CHAI ,  Yong ZHU

IPC: G06F16/9032 ,  G06N3/04 ,  G06F16/906 ,  G06F16/903 ,  G06F40/30

Abstract: Provided by the present disclosure is a new category tag mining method, involving the field of knowledge graph technology, and including: obtaining a plurality of queries during a current preset time period; labeling a category tag on each query of the plurality of queries, by using a pre-trained sequence labeling model, to extract the category tag currently corresponding to the query from the query; and removing a category tag already existing in a preset current category tag library from category tags currently corresponding to all the queries, and determining a remaining category tag as a new category tag. The present disclosure also provides an electronic device and a non-transitory computer-readable storage medium.

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：US20210192142A1

公开(公告)日：2021-06-24

申请号：US17024756

申请日：2020-09-18

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

Inventor： Zhifan FENG ,  Haifeng WANG ,  Kexin REN ,  Yong ZHU ,  Yajuan LYU

IPC: G06F40/30 ,  G06N3/08 ,  G06N3/04

Abstract: The present disclosure discloses a multimodal content processing method, apparatus, device and storage medium, which relate to the technical field of artificial intelligence. The specific implementation is: receiving a content processing request of a user which is configured to request semantic understanding of multimodal content to be processed, analyzing the multimodal content to obtain the multimodal knowledge nodes corresponding to the multimodal content, determining a semantic understanding result of the multimodal content according to the multimodal knowledge nodes, a pre-constructed multimodal knowledge graph and the multimodal content, the multimodal knowledge graph including: the multimodal knowledge nodes and an association relationship between multimodal knowledge nodes. The technical solution can obtain an accurate semantic understanding result, realize an accurate application of multimodal content, and solve the problem in the prior art that multimodal content understanding is inaccurate.