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公开(公告)号:US20210406476A1
公开(公告)日:2021-12-30
申请号:US17113748
申请日:2020-12-07
Inventor: Lu PAN , Yuguang CHEN , Fayuan LI
IPC: G06F40/30 , G06F40/289 , G06F40/284
Abstract: The disclosure provides a method for extracting an event from a text, an electronic device and a storage medium, relate to fields of knowledge graph, deep learning, and natural language processing. The method includes: obtaining an input text; inputting the input text into a model for extracting trigger words to obtain a trigger word extraction result of the input text; inputting the input text and the trigger word extraction result into a model for extracting arguments to obtain an argument extraction result of the input text; and obtaining an event extraction result of the input text according to the trigger word extraction result and the argument extraction result.
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公开(公告)号:US20210200947A1
公开(公告)日:2021-07-01
申请号:US17201769
申请日:2021-03-15
Inventor: Fayuan LI , Yuguang CHEN , Lu PAN , Yuanzhen LIU , Cuiyun HAN , Xi SHI , Jiayan HUANG
IPC: G06F40/205 , G06F40/169 , G06F40/30
Abstract: An event argument extraction method, an event argument extraction apparatus and an electronic device are disclosed, which relate to the field of artificial intelligence. A specific implementation is: acquiring to-be-extracted event content; and performing an argument extraction on the to-be-extracted event content based on a trained event argument extraction model, to acquire an target argument of the to-be-extracted event content; where, the trained event argument extraction model is acquired by training an intermediate extraction model by using event news annotation data, and the intermediate extraction model is acquired by training a pre-trained model by using event news samples and reading comprehension data.
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公开(公告)号:US20210406295A1
公开(公告)日:2021-12-30
申请号:US17105707
申请日:2020-11-27
Inventor: Shangru ZHONG , Yuguang CHEN , Weihua PENG
IPC: G06F16/33 , G06F40/279 , G06F16/332 , G06K9/62
Abstract: A method for generating a relationship of events includes: obtaining a statement of a first event and a statement of a second event; generating a word sequence vector with first granularity and a word sequence vector with second granularity based on the statement of the first event; generating a word sequence vector with third granularity and a word sequence vector with fourth granularity based on the statement of the second event; generating a first fusion vector based on the word sequence vector with first granularity and the word sequence vector with second granularity; generating a second fusion vector based on the word sequence vector with third granularity and the word sequence vector with fourth granularity; and determining a relationship between the first event and the second event based on the first fusion vector and the second fusion vector.
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