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公开(公告)号：US20230139642A1
公开(公告)日：2023-05-04
申请号：US18089792
申请日：2022-12-28
Inventor： Kaichun YAO , Hengshu ZHU , Peng WANG , Xin SONG , Jingshuai ZHANG , Chuan QIN , Jing WANG
IPC: G06F40/289 , G06F40/35 , G06F40/205
Abstract: A method and an apparatus for extracting a skill label, and a method and an apparatus for training a candidate phrase classification model are provided. The method for extracting the skill label includes obtaining a plurality of words by performing word segmentation on a sentence to be extracted, and determining a multi-dimensional feature vector of each word; extracting a candidate phrase from the sentence to be extracted; determining a multi-dimensional feature vector of each word in the candidate phrase according to the multi-dimensional feature vector of each word; generating a semantic representation vector of the candidate phrase according to the multi-dimensional feature vector of each word in the candidate phrase; and extracting the skill label from the sentence to be extracted based on the semantic representation vector of the candidate phrase.
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公开(公告)号：US20220282992A1
公开(公告)日：2022-09-08
申请号：US17717872
申请日：2022-04-11
Inventor： Yanyan LI , Peng WANG , Hengshu ZHU , Shilin WU , Dejing DOU
Abstract: The present disclosure provides a method and apparatus for generating an electronic map, an electronic device and a storage medium, and relates to the field of data processing technology. A specific implementation comprises: establishing a plurality of groups of corresponding relationships between enterprise names and enterprise addresses using network data; determining respectively a fine-grained region where each enterprise address is located; and creating an enterprise electronic map of the fine-grained region based on the corresponding relationships between the enterprise names and the enterprise addresses and the fine-grained region where the each enterprise address is located. A corresponding relationship between an enterprise name and an enterprise address is established using existing network data.
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3.发明公开公开(公告)号：US20230230038A1
公开(公告)日：2023-07-20
申请号：US17945168
申请日：2022-09-15
Inventor： Ying SUN , Hengshu ZHU , Chuan QIN , Peng WANG , Hui XIONG
IPC: G06Q10/10 , G06F16/906
CPC classification number: G06Q10/1053 , G06F16/906
Abstract: There is provided a method for cross-regional talent flow intention analysis, an electronic device, and a storage medium, which relates to technical fields such as big data processing and data statistics and analysis. A specific implementation solution involves: constructing a talent flow intention network based on search data in a network within a preset period of time; and performing cross-regional talent flow intention analysis based on the talent flow intention network to obtain a talent flow intention analysis result.
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公开(公告)号：US20220392242A1
公开(公告)日：2022-12-08
申请号：US17819838
申请日：2022-08-15
Abstract: A method for training a text positioning model includes: obtaining a sample image, where the sample image contains a sample text to be positioned and a text marking box for the sample text; inputting the sample image into a text positioning model to be trained to position the sample text, and outputting a prediction text box for the sample image; obtaining a sample prior anchor box corresponding to the sample image; and adjusting model parameters of the text positioning model based on the sample prior anchor box, the text marking box and the prediction text box, and continuing training the adjusted text positioning model based on a next sample image until model training is completed, to generate a target text positioning model.
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5.发明申请公开(公告)号：US20220114822A1
公开(公告)日：2022-04-14
申请号：US17559643
申请日：2021-12-22
Inventor： Chao MA , Jingshuai ZHANG , Qifan HUANG , Kaichun YAO , Peng WANG , Hengshu ZHU
IPC: G06V30/148 , G06V30/41 , G06V30/262 , G06V30/18
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.
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Patent Agency Ranking
6.发明申请公开(公告)号：US20220415195A1
公开(公告)日：2022-12-29
申请号：US17899831
申请日：2022-08-31
Inventor： Chao WANG , Hengshu ZHU , Peng WANG , Xin SONG
Abstract: A method for training a course recommendation model, a method for course recommendation, and an apparatus, which relate to a field of big data and deep learning in a field of artificial intelligence technology, and can be applied to recommendation scenarios. The training method includes: obtaining a sample data set, where the sample data set includes user learning data, the user learning data includes record data and ability label data, the record data is used for representing a historical learning process of a sample user, and the ability label data is used for representing a learning ability level of the sample user, and training and generating the course recommendation model according to the user learning data, where the course recommendation model is used for recommending a course for a user, the technical effect of improving the reliability and accuracy of course recommendation is achieved.
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公开(公告)号：US20220122022A1
公开(公告)日：2022-04-21
申请号：US17564372
申请日：2021-12-29
Inventor： Kaichun YAO , Jingshuai ZHANG , Hengshu ZHU , Chuan QIN , Chao MA , Peng WANG
Abstract: The present disclosure provides a method of processing data, a device and a computer-readable storage medium, which relates to a technical field of artificial intelligence, and in particular to fields of intelligent search and deep learning. The method includes: generating a resume heterogeneous graph and a job heterogeneous graph; determining a first matching feature representation for the resume and the job profile based on first and second node feature representations for a first node in the resume heterogeneous graph and a second node in the job heterogeneous graph respectively; determining a second matching feature representation for the resume and the job profile based on first and second graph feature representations for the resume heterogeneous graph and the job heterogeneous graph respectively; and determining a similarity between the resume and the job profile based on the first and second matching feature representations.
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公开(公告)号：US20220121826A1
公开(公告)日：2022-04-21
申请号：US17564369
申请日：2021-12-29
Inventor： Chao MA , Jingshuai ZHANG , Qifan HUANG , Kaichun YAO , Peng WANG , Hengshu ZHU
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.
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公开(公告)号：US20230004715A1
公开(公告)日：2023-01-05
申请号：US17939271
申请日：2022-09-07
Inventor： Peng WANG , Hengshu ZHU , Zheng DONG , Kaichun YAO , Chuan QIN
IPC: G06F40/279 , G06F40/253 , G06F16/33
Abstract: A method and an apparatus for constructing an object relationship network and an electronic device are provided by the present disclosure, relating to the field of artificial intelligence technologies, such as deep neural networks, deep learning, etc. A specific implementation solution is: extracting keywords in respective text contents corresponding to a plurality of objects to obtain keywords corresponding to respective objects; and according to the keywords corresponding to the objects, a similarity between the plurality of objects is determined; and then according to the similarity between the plurality of objects, an object relationship network between the plurality of objects is constructed. Since the object relationship network constructed by means of the similarity between the plurality of objects can accurately describe a closeness degree of a relationship between the objects, thus, the plurality of objects can be managed effectively by means of the constructed object relationship network.
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公开(公告)号：US20230230035A1
公开(公告)日：2023-07-20
申请号：US17940544
申请日：2022-09-08
Inventor： Peng WANG , Zheng DONG , Hengshu ZHU , Xin SONG , Jing WANG , Jingshuai ZHANG
CPC classification number: G06Q10/101 , G06K9/6276 , G06K9/6272 , G06K9/6215 , G06Q50/01
Abstract: The present disclosure provides a method and apparatus for constructing an organizational collaboration network, and relates to the field of artificial intelligence, and particularly to the field of big data analysis. A specific implementation includes: acquiring collaborative data between at least one pair of organizations; calculating at least one collaboration index between each pair of organizations according to the collaborative data; calculating, for each pair of organizations, a degree of closeness between the pair of organizations according to a weighted sum of the at least one collaboration index between the pair of organizations; and using each organization as a node, a relationship between each pair of organizations as an edge, and the degree of closeness between each pair of organizations as a weight of the edge, to construct the organizational collaboration network.
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