公开(公告)号：US11899699B2

公开(公告)日：2024-02-13

申请号：US17347448

申请日：2021-06-14

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

Inventor： Kaichun Yao ,  Chuan Qin ,  Hengshu Zhu ,  Chao Ma ,  Jingshuai Zhang

IPC: G06F40/289 ,  G06F40/40 ,  G06F16/332 ,  G06F16/335 ,  G06F40/20

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.

公开(公告)号：US12277397B2

公开(公告)日：2025-04-15

申请号：US17564369

申请日：2021-12-29

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

Inventor： Chao Ma ,  Jingshuai Zhang ,  Qifan Huang ,  Kaichun Yao ,  Peng Wang ,  Hengshu Zhu

IPC: G06F40/40 ,  G06F40/30 ,  G06N7/01

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.

公开(公告)号：US12265950B2

公开(公告)日：2025-04-01

申请号：US17564363

申请日：2021-12-29

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

Inventor： Jingshuai Zhang ,  Qifan Huang ,  Chao Ma ,  Hengshu Zhu ,  Peng Wang ,  Kaichun Yao ,  Jing Wang

IPC: G06Q10/1053 ,  G06N3/08

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.

公开(公告)号：US11989962B2

公开(公告)日：2024-05-21

申请号：US17559643

申请日：2021-12-22

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

Inventor： Chao Ma ,  Jingshuai Zhang ,  Qifan Huang ,  Kaichun Yao ,  Peng Wang ,  Hengshu Zhu

IPC: G06V30/00 ,  G06V30/148 ,  G06V30/18 ,  G06V30/262 ,  G06V30/41

CPC classification number: G06V30/153 ,  G06V30/18181 ,  G06V30/274 ,  G06V30/41

Abstract: A method, an apparatus, a device, a storage medium and a program product of performing a text matching are provided, which relate to a field of a computer technology, and in particular to natural language processing and deep learning technologies. The method includes: determining a word set and a plurality of semantic units from a text set, the word set is associated with a first predetermined attribute, and the text set contains a plurality of first texts indicating an object information and a plurality of second texts indicating an object demand information; generating a graph; and generating a final feature representation associated with the text set and the word set based on the graph and a graph convolution model, so as to perform the text matching.