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公开(公告)号:US11727216B2
公开(公告)日:2023-08-15
申请号:US17117553
申请日:2020-12-10
Inventor: Zhijie Liu , Qi Wang , Zhifan Feng , Chunguang Chai , Yong Zhu
IPC: G06F17/00 , G06F40/30 , G06F40/295 , G06F17/16
CPC classification number: G06F40/30 , G06F17/16 , G06F40/295
Abstract: A method, apparatus, device, and storage medium for linking an entity, relates to the technical fields of knowledge graph and deep learning are provided. The method may include: acquiring a target text; determining at least one entity mention included in the target text and a candidate entity corresponding to each entity mention; determining an embedding vector of each candidate entity based on the each candidate entity and a preset entity embedding vector determination model; determining context semantic information of the target text based on the target text and each embedding vector; determining type information of the at least one entity mention; and determining an entity linking result of the at least one entity mention, based on the each embedding vector, the context semantic information, and each type information.
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公开(公告)号:US20210216716A1
公开(公告)日:2021-07-15
申请号:US17213927
申请日:2021-03-26
Inventor: Qi Wang , Zhifan Feng , Zhijie Liu , Siqi Wang , Chunguang Chai , Yong Zhu
IPC: G06F40/295 , G06F16/33
Abstract: A method, apparatus, device, and storage medium for entity linking is disclosed. The method includes: acquiring a target text; determining at least one entity mention included in the target text; determining a candidate entity corresponding to each of the entity mention based on a preset knowledge base; determining a reference text of each of the candidate entity and determining additional feature information of each of the candidate entity; and determining an entity linking result based on the target text, each of the reference text, and each piece of the additional feature information.
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公开(公告)号:US11651164B2
公开(公告)日:2023-05-16
申请号:US17036609
申请日:2020-09-29
Inventor: Zhijie Liu , Qi Wang , Zhifan Feng , Zhou Fang , Chunguang Chai , Yong Zhu
IPC: G06F40/30 , G06F16/435 , G06F16/2458 , G06F40/253 , G06F40/279
CPC classification number: G06F40/30 , G06F16/2465 , G06F16/435 , G06F40/253 , G06F40/279 , G06F2216/03
Abstract: The present disclosure provides a method, a device, an equipment and a storage medium for mining a topic concept. The method includes: acquiring a plurality of candidate topic concepts based on a query; performing word segmentation on the plurality of candidate topic concepts and performing part-of-speech tagging on words obtained after performing the word segmentation, to obtain a part-of-speech sequence of each of the plurality of candidate topic concepts; and filtering the plurality of candidate topic concepts based on the part-of-speech sequence, to filter out a topic concept corresponding to a target part-of-speech sequence among the plurality of candidate topic concepts, in which a proportion of accurate topic concepts in the target part-of-speech sequence is lower than or equal to a first preset threshold, or a proportion of inaccurate topic concepts in the target part-of-speech sequence is higher than or equal to a second preset threshold.
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公开(公告)号:US20210326535A1
公开(公告)日:2021-10-21
申请号:US17036609
申请日:2020-09-29
Inventor: Zhijie Liu , Qi Wang , Zhifan Feng , Zhou Fang , Chunguang Chai , Yong Zhu
IPC: G06F40/30 , G06F40/253 , G06F40/279 , G06F16/2458 , G06F16/435
Abstract: The present disclosure provides a method, a device, an equipment and a storage medium for mining a topic concept. The method includes: acquiring a plurality of candidate topic concepts based on a query; performing word segmentation on the plurality of candidate topic concepts and performing part-of-speech tagging on words obtained after performing the word segmentation, to obtain a part-of-speech sequence of each of the plurality of candidate topic concepts; and filtering the plurality of candidate topic concepts based on the part-of-speech sequence, to filter out a topic concept corresponding to a target part-of-speech sequence among the plurality of candidate topic concepts, in which a proportion of accurate topic concepts in the target part-of-speech sequence is lower than or equal to a first preset threshold, or a proportion of inaccurate topic concepts in the target part-of-speech sequence is higher than or equal to a second preset threshold.
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公开(公告)号:US11995117B2
公开(公告)日:2024-05-28
申请号:US17069410
申请日:2020-10-13
Inventor: Qi Wang , Zhifan Feng , Zhijie Liu , Chunguang Chai , Yong Zhu
CPC classification number: G06F16/45
Abstract: A theme classification method based on multimodality is related to a field of a knowledge map. The method includes obtaining text information and non-text information of an object to be classified. The non-text information includes at least one of visual information and audio information. The method also includes determining an entity set of the text information based on a pre-established knowledge base, and then extracting a text feature of the object based on the text information and the entity set. The method also includes determining a theme classification of the object based on the text feature and a non-text feature of the object.
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公开(公告)号:US11704492B2
公开(公告)日:2023-07-18
申请号:US17213927
申请日:2021-03-26
Inventor: Qi Wang , Zhifan Feng , Zhijie Liu , Siqi Wang , Chunguang Chai , Yong Zhu
IPC: G06F40/295 , G06F16/36 , G06Q10/04 , G06F16/33 , G06F40/30
CPC classification number: G06F40/295 , G06F16/3344 , G06F40/30
Abstract: A method, apparatus, device, and storage medium for entity linking is disclosed. The method includes: acquiring a target text; determining at least one entity mention included in the target text; determining a candidate entity corresponding to each of the entity mention based on a preset knowledge base; determining a reference text of each of the candidate entity and determining additional feature information of each of the candidate entity; and determining an entity linking result based on the target text, each of the reference text, and each piece of the additional feature information, wherein determining the entity linking result includes determining a probability of linking each of the candidate entity to the entity mention based on a splicing of a first embedding vector and a second embedding vector of the target text and a splicing of a first embedding vector and a second embedding vector of each respective reference text.
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