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公开(公告)号：US11521016B2
公开(公告)日：2022-12-06
申请号：US16700593
申请日：2019-12-02
发明人： Miao Fan , Sen Ye , Chao Feng , Mingming Sun , Ping Li , Haifeng Wang
摘要： Embodiments of the present disclosure provide a method for generating an information assessment model, a method for determining the usefulness of comment information, apparatus, electronic device, and computer-readable medium. The method may include: acquiring training samples, the training samples including first sample comment information with a usefulness label and second sample comment information without a usefulness label; acquiring a predictor model and a discriminator model respectively constructed based on a generative network and a discrimination network in a generative adversarial network, and pre-training the predictor model using the first sample comment information, the predictor model being used to predict a usefulness label of a piece of comment information, the discriminator model being used to discriminate authenticity of a usefulness label; and training the predictor model and the discriminator model by iteratively performing a plurality of times of training operations, using the trained predictor model as an information assessment model.
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公开(公告)号：US11514247B2
公开(公告)日：2022-11-29
申请号：US16713062
申请日：2019-12-13
发明人： Miao Fan , Sen Ye , Mingming Sun , Ping Li , Haifeng Wang
摘要： A method, an apparatus, a computer device and a readable medium for knowledge hierarchical extraction of a text are disclosed. The method comprises: performing word segmentation on a designated text to obtain a word list, the word list including at least one word arranged in a sequence in the designated text; analyzing part-of-speech of each word in the word list in the designated text, to obtain a part-of-speech list corresponding to the word list; predicting a SPO triple included in the designated text according to the word list, the part-of-speech list and a pre-trained knowledge hierarchical extraction model. By the technical solutions, the SPO triple included in any designated text however loose its organization and structure is may be accurately extracted based on the pre-trained knowledge hierarchical extraction model. Compared to the prior art, the efficiency and accuracy of knowledge hierarchical extraction may be effectively improved.
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