公开(公告)号：US20230401221A1

公开(公告)日：2023-12-14

申请号：US18063509

申请日：2022-12-08

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

Inventor： Lu GAN ,  Zenghui XU ,  Zhiqun XIA ,  Jianbing ZHANG ,  Lianghui CHEN ,  Jian GONG ,  Ke SUN

IPC: G06F16/2458 ,  G06F16/242 ,  G06F16/2453

CPC classification number: G06F16/2458 ,  G06F16/2433 ,  G06F16/24542

Abstract: Provided are a cross-tables search method, an electronic device and a storage medium, relating to a field of artificial intelligence, and in particular, to natural language processing, big data, knowledge graph technology, which may be applied in scenarios of smart cloud, smart city, and smart government affairs. The cross-tables search method includes: parsing a query to obtain an entity, attribute and relationship required to be searched; determining a cross-tables search strategy according to the entity, the attribute and the relationship; and performing a cross-tables search operation according to the cross-tables search strategy.

公开(公告)号：US20230316709A1

公开(公告)日：2023-10-05

申请号：US17902323

申请日：2022-09-02

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

Inventor： Siqi XU ,  Ke SUN ,  Jian GONG ,  Xu PAN ,  Zhiqun XIA ,  Zhe YANG ,  Zecheng ZHUO

IPC: G06V10/762 ,  G06V10/764 ,  G06V10/74 ,  G06F16/28

CPC classification number: G06V10/762 ,  G06V10/764 ,  G06V10/761 ,  G06F16/285

Abstract: Provided is a data labeling method based on artificial intelligence, an apparatus, and a storage medium relating to the field of artificial intelligence, particularly data labeling, image recognition, and natural language processing. The method includes: determining a plurality of samples involved in clustering; performing a plurality of following operations circularly to realize iterative processing, until a convergence condition is satisfied or a quantity of iterations reaches a number threshold, comprising: pre-clustering the plurality of samples according to a vector representation of the respective samples to obtain a plurality of class clusters, each class cluster containing at least one sample; receiving labeling information for the respective class clusters and re-determining the plurality of samples according to the labeling information; and determining a clustering result according to the labeling information for the respective class clusters.