公开(公告)号：US12210982B2

公开(公告)日：2025-01-28

申请号：US17872318

申请日：2022-07-25

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

Inventor： Wenbin Jiang ,  Yajuan Lyu ,  Yong Zhu ,  Hua Wu ,  Haifeng Wang

IPC: G06F40/30 ,  G06F40/20 ,  G06N5/04

Abstract: The present disclosure provides a method for processing intelligent question-answering, an intelligent question-answering system, an electronic device and a storage medium, and relates to the field of artificial intelligence technologies, such as machine learning technologies, natural language processing technologies, or the like. An implementation includes: acquiring an input question and input data information; and based on the question, the data information and a plurality of knowledge bases, deciding an answer to the question by multilayer appreciation using a plurality of understanding module layers.

公开(公告)号：US12019990B2

公开(公告)日：2024-06-25

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: G06N20/00 ,  G06F18/214 ,  G06F18/2413 ,  G06F40/279 ,  G06F40/30 ,  G06N5/022

CPC classification number: G06F40/30 ,  G06F18/214 ,  G06F18/24147 ,  G06F40/279 ,  G06N5/022

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.