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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