CROSS-CONTEXT NATURAL LANGUAGE MODEL GENERATION

    公开(公告)号:US20210295822A1

    公开(公告)日:2021-09-23

    申请号:US17210311

    申请日:2021-03-23

    Applicant: Sorcero, Inc.

    Abstract: Provided is a method including obtaining a corpus and an associated set of domain indicators. The method includes learning a set of vectors in an embedding space based on n-grams of the corpus. The method includes updating ontology graphs comprising a set of vertices and edges associating the set of vertices with each other. The method also includes determining a vector cluster using hierarchical clustering based on distances of the set of vectors with respect to each other in the embedding space and determining a hierarchy of the ontology graphs based on a set of domain indicators of a respective set of vertices corresponding to vectors of the vector cluster. The method also includes updating an index based on the ontology graphs.

    Cross-context natural language model generation

    公开(公告)号:US11151982B2

    公开(公告)日:2021-10-19

    申请号:US17210311

    申请日:2021-03-23

    Applicant: Sorcero, Inc.

    Abstract: Provided is a method including obtaining a corpus and an associated set of domain indicators. The method includes learning a set of vectors in an embedding space based on n-grams of the corpus. The method includes updating ontology graphs comprising a set of vertices and edges associating the set of vertices with each other. The method also includes determining a vector cluster using hierarchical clustering based on distances of the set of vectors with respect to each other in the embedding space and determining a hierarchy of the ontology graphs based on a set of domain indicators of a respective set of vertices corresponding to vectors of the vector cluster. The method also includes updating an index based on the ontology graphs.

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