METHOD AND DEVICE FOR GENERATING RANKING MODEL

    公开(公告)号:US20210026860A1

    公开(公告)日:2021-01-28

    申请号:US16980897

    申请日:2018-09-07

    Abstract: The embodiment of the present application discloses a method and a device for generating a ranking model. A specific embodiment of the method includes: acquiring a sample set, executing following training steps: for the samples in the sample set, inputting the query information, the first position document and the second position document in the sample into an initial model, respectively obtaining scores of the input documents, and determining a target value of the sample based on the obtained scores, a clicked bias of a first position and an unclicked bias of a second position, updating the initial model based on the target value of each sample; determining whether the initial model is completely trained; and in response to determining that the initial model is completely trained, determining the updated initial model as the ranking model.

    Semantic understanding method and apparatus, and device and storage medium

    公开(公告)号:US11776535B2

    公开(公告)日:2023-10-03

    申请号:US17885965

    申请日:2022-08-11

    CPC classification number: G10L15/1815 G10L15/183

    Abstract: A semantic understanding method and apparatus, and a device and a storage medium are provided. The method includes: acquiring a recognition character string that matches speech information; acquiring, from an entity vocabulary library, at least one entity vocabulary respectively corresponding to each recognition character in the recognition character string; and according to a situation of each entity vocabulary hitting the recognition character string, determining a matching entity vocabulary as a semantic understanding result of the speech information. By means of the method, insofar as a completely matching entity vocabulary is not acquired, a matching entity vocabulary can still be determined according to an entity vocabulary library, and semantic information of speech is thus accurately understood; and the method also has relatively high fault tolerance for situations such as wrong words, added words, and omitted words, such that the semantic understanding accuracy of speech information is improved.

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