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公开(公告)号：US20230016403A1
公开(公告)日：2023-01-19
申请号：US17934876
申请日：2022-09-23
Inventor： Zhaoji WANG , Fang HUANG , Ye JIANG , Yabing SHI , Chunguang CHAI , Yong ZHU
IPC: G06F16/9537 , G06F40/226 , G06F40/30
Abstract: The present disclosure provides a method of processing triple data, a method of training a triple data processing model, an electronic device, and a storage medium. A specific implementation solution includes: performing a triple data extraction on text data to obtain a plurality of field data; normalizing the plurality of field data to determine target triple data, wherein the target triple data contains entity data, entity relationship data, and association entity data; and verifying a confidence level of the target triple data to obtain a verification result.
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2.发明公开公开(公告)号：US20230153337A1
公开(公告)日：2023-05-18
申请号：US18157452
申请日：2023-01-20
Inventor： Wenbin JIANG , Yajuan LV , Chunguang CHAI , Yong ZHU
IPC: G06F16/332 , G06F40/30
CPC classification number: G06F16/3329 , G06F40/30
Abstract: A question answering method, a method of training a question answering model, a device, and a medium are provided, which relate to a field of artificial intelligence technology, in particular to fields of natural language processing technology, deep learning technology, and knowledge mapping technology. The question answering method includes: obtaining data to be processed, wherein the data to be processed includes a question and candidate answers; performing general semantic understanding on the data to be processed to obtain a general data feature; selecting a target question answering mode from candidate question answering modes based on the general data feature; and processing the general data feature by using the target question answering mode, to obtain a target answer for the question from the candidate answers.
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公开(公告)号：US20220350965A1
公开(公告)日：2022-11-03
申请号：US17864636
申请日：2022-07-14
Inventor： Tongyang LIU , Shu WANG , Wanli CHANG , Wei ZHENG , Zhifan FENG , Chunguang CHAI , Yong ZHU
IPC: G06F40/211 , G06F40/30 , G06F40/109 , G06N3/08
Abstract: A method for generating a pre-trained language model, includes: obtaining sample files; obtaining typography structure information and text information of the sample files by parsing the sample files; obtaining a plurality of task models of a pre-trained language model; obtaining a trained pre-trained language model by jointly training the pre-trained language model and the plurality of task models according to the typography structure information and the text information; and generating a target pre-trained language model by fine-tuning the trained pre-trained language model according to the typography structure information and the text information.
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公开(公告)号：US20230038091A1
公开(公告)日：2023-02-09
申请号：US17957188
申请日：2022-09-30
Inventor： Yue ZHANG , Zhou FANG , Yabing SHI , Ye JIANG , Chunguang CHAI
IPC: G06F16/906 , G06F16/22 , G06F40/177 , G06F40/20
Abstract: A method of extracting a table information, an electronic device, and a storage medium are provided, which relate to fields of artificial intelligence and big data, in particular to fields of machine learning, knowledge graph, intelligent search and intelligent recommendation, and may be used for an intelligent extraction of an information in a table and other scenarios. The method includes: performing a clustering based on features of a plurality of rows of cells and/or features of a plurality of columns of cells in a table, so as to determine candidate header cells in the table; and performing an information extraction on the table based on the candidate header cells, so as to extract attribute-attribute value pairs in the table.
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公开(公告)号：US20230005284A1
公开(公告)日：2023-01-05
申请号：US17943458
申请日：2022-09-13
Inventor： Feng HE , Qi WANG , Hu YANG , Shuai CHEN , Zhifan FENG , Chunguang CHAI
IPC: G06V30/19 , G06F16/583
Abstract: A computer-implemented method is provided. The method includes: obtaining a sample text and a sample image corresponding to the sample text; labeling a true semantic tag for the sample text according to a first preset rule; obtaining a text feature representation of the sample text and a predicted semantic tag output by a text coding sub-model; obtaining an image feature representation of the sample image output by an image coding sub-model; calculating a first loss based on the true semantic tag and the predicted semantic tag; calculating a contrast loss based on the text feature representation of the sample text and the image feature representation of the sample image; adjusting parameters of the text coding sub-model based on the first loss and the contrast loss; and adjusting parameters of the image coding sub-model based on the contrast loss.
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Patent Agency Ranking
6.发明申请公开(公告)号：US20210263974A1
公开(公告)日：2021-08-26
申请号：US17173318
申请日：2021-02-11
Inventor： Qian LI , Yabing SHI , Ye JIANG , Chunguang CHAI , Yong ZHU
IPC: G06F16/9032 , G06N3/04 , G06F16/906 , G06F16/903 , G06F40/30
Abstract: Provided by the present disclosure is a new category tag mining method, involving the field of knowledge graph technology, and including: obtaining a plurality of queries during a current preset time period; labeling a category tag on each query of the plurality of queries, by using a pre-trained sequence labeling model, to extract the category tag currently corresponding to the query from the query; and removing a category tag already existing in a preset current category tag library from category tags currently corresponding to all the queries, and determining a remaining category tag as a new category tag. The present disclosure also provides an electronic device and a non-transitory computer-readable storage medium.
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7.发明申请公开(公告)号：US20230133717A1
公开(公告)日：2023-05-04
申请号：US17954900
申请日：2022-09-28
Inventor： Jiandong SUN , Yabing SHI , Ye JIANG , Chunguang CHAI
IPC: G06F40/289 , G06F40/30
Abstract: Disclosed are an information extraction method, an electronic device and a readable storage medium, which relate to the field of artificial intelligence technologies, and particularly to the field of knowledge graph technologies. The information extraction method includes: acquiring to-be-processed text to obtain a semantic vector of each token in the to-be-processed text; generating a relationship prediction matrix, an entity prediction matrix and an alignment matrix according to each token in the to-be-processed text and the semantic vector of each token; and extracting a target triplet in the to-be-processed text using the relationship prediction matrix, the entity prediction matrix and the alignment matrix, and taking the target triplet as an information extraction result of the to-be-processed text.
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公开(公告)号：US20230010160A1
公开(公告)日：2023-01-12
申请号：US17945415
申请日：2022-09-15
Inventor： Shuai CHEN , Qi WANG , Hu YANG , Feng HE , Zhifan FENG , Chunguang CHAI , Yong ZHU
Abstract: Disclosed are a method for processing multimodal data using a neural network, a device, and a medium, and relates to the field of artificial intelligence and, in particular to multimodal data processing, video classification, and deep learning. The neural network includes: an input subnetwork configured to receive the multimodal data to output respective first features of a plurality of modalities; a plurality of cross-modal feature subnetworks, each of which is configured to receive respective first features of two corresponding modalities to output a cross-modal feature corresponding to the two modalities; a plurality of cross-modal fusion subnetworks, each of which is configured to receive at least one cross-modal feature corresponding to a corresponding target modality and other modalities to output a second feature of the target modality; and an output subnetwork configured to receive respective second features of the plurality of modalities to output a processing result of the multimodal data.
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公开(公告)号：US20220358110A1
公开(公告)日：2022-11-10
申请号：US17871666
申请日：2022-07-22
Inventor： Yue ZHANG , Yabing SHI , Ye JIANG , Chunguang CHAI
IPC: G06F16/22 , G06F16/242
Abstract: A method and apparatus for processing a table, a device, a storage medium and a product. An implementation of the method comprise: receiving a content query request for a target table; acquiring a target tree structure of the target table according to the content query request; where, the target tree structure is obtained by performing absorbing processing and merging processing on at least one target cell in the target table; acquiring to-be-queried content in the content query request; and querying target content matching the to-be-queried content from the target tree structure.
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公开(公告)号：US20220284246A1
公开(公告)日：2022-09-08
申请号：US17502385
申请日：2021-10-15
Inventor： Feng HE , Qi WANG , Zhifan FENG , Hu YANG , Chunguang CHAI
IPC: G06K9/62
Abstract: The present disclosure discloses a method for training a cross-modal retrieval model, an electronic device and a storage medium, and relates to the field of computer technologies, and particularly to the field of artificial intelligence technologies, such as knowledge graph technologies, computer vision technologies, deep learning technologies, or the like. The method for training a cross-modal retrieval model includes: determining similarity of a cross-modal sample pair according to the cross-modal sample pair, the cross-modal sample pair including a sample of a first modal and a sample of a second modal, and the first modal being different from the second modal; determining a soft margin based on the similarity, and determining a soft margin loss function based on the soft margin; and determining a total loss function based on the soft margin loss function, and training a cross-modal retrieval model according to the total loss function.
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