Abstract: Provided are a classification method and apparatus, an electronic device and a storage medium, which relate to the field of artificial intelligence and in particular, to the fields of natural language processing and deep learning. The classification method comprises: performing coding processing on to-be-classified data to obtain a to-be-classified coding feature; determining reference coding features of reference classification data similar to the to-be-classified data according to the to-be-classified coding feature; and determining a target category of the to-be-classified data according to the reference coding features and reference categories of the reference classification data.

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.

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.

Abstract: A task processing method, a task processing device and an electronic device are provided, which relate to the field of cloud computing technology and big data technology, in particular to the field of task processing technology. The task processing method includes: obtaining a task processing request for a to-be-processed task, the task processing request including processing time information of the to-be-processed task and a service type of the to-be-processed task; in the case that the processing time information of the to-be-processed task meets a triggering condition, writing the to-be-processed task into a corresponding message queue in accordance with the service type of the to-be-processed task, one message queue corresponding to a respective one service type; and processing the to-be-processed task in the message queue, to obtain a task processing result of the to-be-processed task.

Abstract: A method of processing event data, a device, and a medium are provided, which relate to fields of deep learning, natural language processing, cloud services, etc. The method of processing event data includes: determining target feature data corresponding to target event data, in response to receiving a query request containing the target event data; selecting correlated feature data from candidate feature data based on the target feature data, wherein a similarity between the correlated feature data and the target feature data meets a preset similarity condition; determining operation data associated with the correlated feature data, wherein the operation data represents a level of attention to correlated event data corresponding to the correlated feature data; and determining a level of attention to the target event data based on the operation data.

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.

Abstract: The present disclosure provides a method and apparatus for constructing an event library, which relate to the technical fields of deep learning, natural language processing, big data, and the like. An implementation includes: acquiring at least one event text data, the at least one event text data being to be assigned to a text library; obtaining an extraction event name based on the event text data; performing a match between the extraction event name and event information in the text library, to obtain a recalled event in the text library; detecting, based on the recalled event, whether the extraction event name meets a unifying condition; and in response to detecting that the extraction event name does not meet the unifying condition, obtaining a new event name in the event library based on the extraction event name, and adding the event text data as a new event into the text library.

Abstract: The present disclosure provides a method and apparatus for obtaining picture public opinions, a computer device and a storage medium, wherein the method comprises: obtaining an information source and a monitored entity; obtaining real-time stream data from the information source; for each picture in the real-time stream data, performing predetermined content recognition respectively for the picture to obtain a recognition result; determining whether the picture matches with the monitored entity according to the recognition result, and generating and storing public opinion information corresponding to the picture if the picture matches with the monitored entity. The solution of the present disclosure can be employed to obtain picture-like public opinion information.

Abstract: The present disclosure provides a method and apparatus for obtaining video public opinions, a computer device and a storage medium, wherein the method comprises: obtaining an information source and a monitored entity; obtaining real-time stream data from the information source; for each video in the real-time stream data, performing predetermined content recognition respectively for the video to obtain a recognition result; determining whether the video matches with the monitored entity according to the recognition result, and generating and storing public opinion information corresponding to the video if the video matches with the monitored entity. The solution of the present disclosure can be employed to obtain video-like public opinion information.

Abstract: A method of monitoring network links is disclosed which includes: issuing, by a primary node module, a configuration file to a plurality of secondary node modules; receiving, by the secondary node modules, the configuration file, and performing a least link coverage algorithm to the configuration file, obtaining a set of least link coverages of a data detection stream between the secondary node modules; performing, by the secondary node modules, a one-way delay detection algorithm, performing one-way delay detection to the set of least link coverages, obtaining a one-way delay detection result, and returning the one-way delay detection result to the primary node module; and determining, by the primary node module, whether to trigger an alarm program based on a set alarm threshold. Links of the entire network are monitored more accurately and efficiently, while solving the asymmetric link delay issue, and may locate a failed link quickly.

Abstract: A method of monitoring network links is disclosed which includes: issuing, by a primary node module, a configuration file to a plurality of secondary node modules; receiving, by the secondary node modules, the configuration file, and performing a least link coverage algorithm to the configuration file, obtaining a set of least link coverages of a data detection stream between the secondary node modules; performing, by the secondary node modules, a one-way delay detection algorithm, performing one-way delay detection to the set of least link coverages, obtaining a one-way delay detection result, and returning the one-way delay detection result to the primary node module; and determining, by the primary node module, whether to trigger an alarm program based on a set alarm threshold. Links of the entire network are monitored more accurately and efficiently, while solving the asymmetric link delay issue, and may locate a failed link quickly.