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公开(公告)号：US20230409626A1
公开(公告)日：2023-12-21
申请号：US17754464
申请日：2021-07-20
发明人： Jizhou Huang , Yibo Sun , Haifeng Wang
IPC分类号： G06F16/387 , G06F40/295
CPC分类号： G06F16/387 , G06F40/295
摘要： The present disclosure discloses a method and apparatus for acquiring point of interest (POI) state information, and relates to a big data technology in the technical field of artificial intelligence. A specific implementation scheme involves: acquiring a text including POI information within a preset period from the Internet; and recognizing the text by using a pre-trained POI state recognition model, to obtain a two-tuple in the text, the two-tuple including a POI name and POI state information corresponding to the POI name The POI state recognition model acquires a vector representation of each first semantic unit in the text, and acquires a vector representation of each second semantic unit in the text based on semantic dependency information of the text; fuses the vector representation of each first semantic unit and the vector representation of each second semantic unit to obtain a fusion vector representation of each first semantic unit; and predicts labels of the POI name and a POI state based on the fusion vector representation of each first semantic unit. The technical solutions of the present disclosure can save labor costs and improve timeliness and accuracy.
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公开(公告)号：US11977574B2
公开(公告)日：2024-05-07
申请号：US17754464
申请日：2021-07-20
发明人： Jizhou Huang , Yibo Sun , Haifeng Wang
IPC分类号： G06F16/387 , G06F40/295
CPC分类号： G06F16/387 , G06F40/295
摘要： A method and apparatus for acquiring point of interest (POI) state information are suggested, which relate to a big data technology in the technical field of artificial intelligence. A specific implementation scheme involves: acquiring a text including POI information within a preset period from the Internet; and recognizing the text by using a pre-trained POI state recognition model, to obtain a two-tuple in the text, the two-tuple including a POI name and POI state information corresponding to the POI name. The POI state recognition model acquires a vector representation of each first semantic unit in the text, and acquires a vector representation of each second semantic unit in the text based on semantic dependency information of the text; fuses the vector representation of each first semantic unit and the vector representation of each second semantic unit to obtain a fusion vector representation of each first semantic unit; and predicts labels of the POI name and a POI state based on the fusion vector representation of each first semantic unit.
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