NATURAL LANGUAGE AND KNOWLEDGE GRAPH-BASED METHOD AND DEVICE FOR REPRESENTATING LEARNING

    公开(公告)号:EP3866025A1

    公开(公告)日:2021-08-18

    申请号:EP20864301.5

    申请日:2020-06-09

    IPC分类号: G06F16/36

    摘要: The present application discloses a text processing method and device based on natural language processing and a knowledge graph, and relates to the in-depth field of artificial intelligence technology. A specific implementation is: an electronic device uses a joint learning model to obtain a semantic representation, which is obtained by the joint learning model by combining knowledge graph representation learning and natural language representation learning, it combines a knowledge graph learning representation and a natural language learning representation, compared to using only the knowledge graph representation learning or the natural language representation learning to learn semantic representation of a prediction object, factors considered by the joint learning model are more in quantity and comprehensiveness, so accuracy of semantic representation can be improved, and thus accuracy of text processing can be improved.

    MULTIMODAL CONTENT PROCESSING METHOD, APPARATUS, DEVICE AND STORAGE MEDIUM

    公开(公告)号:EP3812926A1

    公开(公告)日:2021-04-28

    申请号:EP20196650.4

    申请日:2020-09-17

    IPC分类号: G06F16/36 G06F16/483

    摘要: The present disclosure discloses a multimodal content processing method, apparatus, device and storage medium, which relate to the technical field of artificial intelligence. The specific implementation is: receiving a content processing request of a user which is configured to request semantic understanding of multimodal content to be processed, analyzing the multimodal content to obtain the multimodal knowledge nodes corresponding to the multimodal content, determining a semantic understanding result of the multimodal content according to the multimodal knowledge nodes, a pre-constructed multimodal knowledge graph and the multimodal content, the multimodal knowledge graph including: the multimodal knowledge nodes and an association relationship between multimodal knowledge nodes. The technical solution can obtain an accurate semantic understanding result, realize an accurate application of multimodal content, and solve the problem in the prior art that multimodal content understanding is inaccurate.