DATA LABELING METHOD BASED ON ARTIFICIAL INTELLIGENCE, APPARATUS AND STORAGE MEDIUM

    公开(公告)号:US20230316709A1

    公开(公告)日:2023-10-05

    申请号:US17902323

    申请日:2022-09-02

    CPC classification number: G06V10/762 G06V10/764 G06V10/761 G06F16/285

    Abstract: Provided is a data labeling method based on artificial intelligence, an apparatus, and a storage medium relating to the field of artificial intelligence, particularly data labeling, image recognition, and natural language processing. The method includes: determining a plurality of samples involved in clustering; performing a plurality of following operations circularly to realize iterative processing, until a convergence condition is satisfied or a quantity of iterations reaches a number threshold, comprising: pre-clustering the plurality of samples according to a vector representation of the respective samples to obtain a plurality of class clusters, each class cluster containing at least one sample; receiving labeling information for the respective class clusters and re-determining the plurality of samples according to the labeling information; and determining a clustering result according to the labeling information for the respective class clusters.

    METHOD OF RECOGNIZING ADDRESS, ELECTRONIC DEVICE AND STORAGE MEDIUM

    公开(公告)号:US20230086429A1

    公开(公告)日:2023-03-23

    申请号:US17992884

    申请日:2022-11-22

    Abstract: A method of recognizing an address, an electronic device, and a storage medium, which relate to fields of artificial intelligence and computer technologies, fields of knowledge graph, deep learning and cloud computing. The method includes: performing a location entity recognition on a content to be recognized, so as to obtain a target location entity, the target location entity including at least one of a standardized location entity, an alias location entity, or a landmark location entity; determining a standardized address corresponding to each type of the location entity in the target location entity according to an address graph to obtain at least one standardized address, the address graph including a standardized location entity, an alias location entity, a landmark location entity, and a corresponding relationship between location entities; and determining, from the at least one standardized address, a first target standardized address corresponding to the content to be recognized.

    METHOD AND APPARATUS FOR IDENTIFYING INSTRUCTION, AND SCREEN FOR VOICE INTERACTION

    公开(公告)号:US20220318503A1

    公开(公告)日:2022-10-06

    申请号:US17849369

    申请日:2022-06-24

    Abstract: A method and an apparatus for identifying an instruction, and a screen for voice interaction are provided. The method includes: acquiring a text vector and at least one word importance corresponding to a to-be-identified instruction; selecting a target number of quasi-matching instructions from a preset instruction library based on the text vector and the at least one word importance, where the instruction library includes a correspondence between an instruction and a text vector of the instruction, and the instruction in the instruction library includes an instruction type and an instruction-targeting keyword; and generating, based on the instruction type and the instruction-targeting keyword in the target number of quasi-matching instructions, an instruction type and an instruction-targeting keyword matching the to-be-identified instruction.

    MODEL TRAINING
    7.
    发明申请

    公开(公告)号:US20220198153A1

    公开(公告)日:2022-06-23

    申请号:US17694034

    申请日:2022-03-14

    Abstract: A model training method, a model training platform, an electronic device and a storage medium are provided, which can be used in the field of artificial intelligence, particularly the fields of natural language processing and deep learning. The model training method includes: receiving an input; determining, based on the input, a user-oriented prefabricated function; determining, based on the input, a model training function; determining, based on the input, a pre-trained model; determining, based on the input, a network structure associated with the pre-trained model so as to support use of the pre-trained model; training, based on the input, the model by using the prefabricated function, the model training function, and the pre-trained model; and providing an output associated with a trained model.

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