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公开(公告)号:US20180349350A1
公开(公告)日:2018-12-06
申请号:US15921386
申请日:2018-03-14
Inventor: Zhifan ZHU , Shikun FENG , Kunsheng ZHOU , Jingzhou HE
Abstract: This disclosure discloses an artificial intelligence based method and apparatus for checking a text. An embodiment of the method comprises: lexing a first to-be-checked text and a second to-be-checked text respectively, determining word vectors of the lexed words to generate a first word vector sequence and a second word vector sequence; inputting the first word vector sequence and the second word vector sequence respectively into a pre-trained convolutional neural network containing at least one multi-scale convolutional layer, identifying vector sequences in a plurality of vector sequences outputted by a last multi-scale convolutional layer as eigenvector sequences, to obtain eigenvector sequence groups respectively corresponding to the texts; combining eigenvector sequences in each eigenvector sequence group to generate a combined eigenvector sequence; and analyzing the generated combined eigenvector sequences to determine whether the first text and the second text pass a similarity check. The embodiment improves the flexibility in checking a text.
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公开(公告)号:US20190163737A1
公开(公告)日:2019-05-30
申请号:US16306488
申请日:2016-12-22
Inventor: Kunsheng ZHOU , Jingzhou HE , Lei SHI , Shikun FENG
Abstract: Disclosed are a method and an apparatus for constructing a binary feature dictionary. The method may include: extracting binary features from a corpus; calculating a preset statistic of each binary feature; and selecting a preset number of binary features in sequence according to the preset statistic to constitute the binary feature dictionary.
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公开(公告)号:US20190095447A1
公开(公告)日:2019-03-28
申请号:US16054966
申请日:2018-08-03
Inventor: Qiugen XIAO , Jinbo ZHAN , Kunsheng ZHOU , Liqun ZHENG , Zhihong FU , Jingzhou HE
Abstract: Embodiments of the disclosure disclose a method, apparatus, device, and storage medium for establishing an error correction model based on an error correction platform. The method comprises: determining a target error correction level based on an error correction need of a user; and selecting at least one error correction module from each of at least two error correcting portions of the error correction platform based on the target error correction level, and combining the selected error correction module to form an error correction model.
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