METHODS FOR COMMUNITY SEARCH, ELECTRONIC DEVICE AND STORAGE MEDIUM

    公开(公告)号:US20230140148A1

    公开(公告)日:2023-05-04

    申请号:US18148775

    申请日:2022-12-30

    Abstract: A method for community search is performed by an electronic device. The method includes: obtaining graph data to be processed, in which the graph data includes a plurality of nodes and a plurality of connection edges between the nodes; determining a query node from the plurality of nodes based on the graph data, and determining a target community to which the query node belongs by performing a community search for the query node, in which the target community includes the query node, and at least one node other than the query node in the plurality of nodes; and determining the query node and performing the community search for the query node repeatedly until the community to which each node included in the graph data belongs is determined.

    Keyword generating method, apparatus, device and storage medium

    公开(公告)号:US11899699B2

    公开(公告)日:2024-02-13

    申请号:US17347448

    申请日:2021-06-14

    CPC classification number: G06F16/3329 G06F16/335 G06F40/20

    Abstract: This application discloses a keyword generating method, an apparatus, a device and a storage medium, which relate to the field of natural language processing in the field of artificial intelligence. A specific implementation scheme includes: inputting a target text into a text processing model, obtaining a word sequence corresponding to the target text, and generating a semantic representation sequence corresponding to the word sequence; making prediction about each semantic representation vector in the semantic representation sequence respectively to obtain a prediction result; and if the prediction result indicates that a word corresponding to the semantic representation vector is capable of triggering a generation of a keyword, outputting the keyword based on the semantic representation vector and the prediction result. This method improves the accuracy of generating keywords.

    Method of training model, method of determining word vector, device, medium, and product

    公开(公告)号:US12277397B2

    公开(公告)日:2025-04-15

    申请号:US17564369

    申请日:2021-12-29

    Abstract: A method of training a model, a method of determining a word vector, a device, a medium, and a product are provided, which may be applied to fields of natural language processing, information processing, etc. The method includes: acquiring a first word vector set corresponding to a first word set; and generating a reduced-dimensional word vector for each word vector in the first word vector set based on a word embedding model, generating, for other word vector in the first word vector set, a first probability distribution in the first word vector set based on the reduced-dimensional word vector, and adjusting a parameter of the word embedding model so as to minimize a difference between the first probability distribution and a second probability distribution for the other word vector determined by a number of word vector in the first word vector set.

    Method and apparatus of matching data, device and computer readable storage medium

    公开(公告)号:US12265950B2

    公开(公告)日:2025-04-01

    申请号:US17564363

    申请日:2021-12-29

    Abstract: The present disclosure provides a method and an apparatus of matching data, a device and a computer-readable storage medium, which are related to the field of artificial intelligence technology, and in particularly to the field of intelligent search and deep learning. The specific implementation solution includes: obtaining a first instance of a resume and a second instance of a job profile; determining, for a meta path, a resume feature data of the first instance and a profile feature data of the second instance, the meta path is a knowledge graph path from the resume to the job profile; and applying a classification model to the resume feature data of the first instance and the profile feature data of the second instance to determine a matching result between the first instance and the second instance.

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