Abstract: The present disclosure relates to a corpus processing method, a corpus processing apparatus and a storage medium. The corpus processing method can include obtaining a message input by a user, retrieving a reply message matching the message input by the user from a plurality of candidate corpora, in which the plurality of the candidate corpora includes candidate corpora obtained after removing a negative emotion corpus, and sending the reply message.

Abstract: Provided are a method and apparatuses for training a classification neural network, a text classification method and apparatus and an electronic device. The method includes: acquiring a regression result of sample text data, which is determined based on a pre-constructed first target neural network and represents a classification trend of the sample text data; inputting the sample text data and the regression result to a second target neural network; obtaining a predicted classification result of each piece of sample text data based on the second target neural network; adjusting a parameter of the second target neural network according to a difference between the predicted classification result and a true value of a corresponding category; and obtaining a trained second target neural network after a change of network loss related to the second target neural network meets a convergence condition.

Abstract: A method for training classification model is provided. The method includes: an annotated data set is processed based on a pre-trained first model, to obtain N first class probabilities, each being a probability that the annotated sample data is classified as a respective one of N classes; maximum K first class probabilities are selected from the N first class probabilities, and K first prediction labels, each corresponding to a respective one of K first class probabilities, are determined; and a second model is trained based on the annotated data set, a real label of each of the annotated sample data and the K first prediction labels of each of the annotated sample data. A classification method and device for training classification model are also provided.

Abstract: Text content is determined. The text content is input to a content classifying model. The content classifying model is adapted to determine a probability of the text content belonging to a category. An evaluated value of quality of the text content is determined according to the probability of the category and a weight of the category. The weight represents importance of the category.

Abstract: The present disclosure relates to an image processing method that can include that an image to be processed is acquired, at least one category is selected via category information of the image to be processed, and a thermodynamic diagram for each category in the at least one category can be determined via the category information. The method can further include that a first positioning frame set corresponding to a target object in the image to be processed is respectively determined for the thermodynamic diagram for each category in the thermodynamic diagram for the at least one category, and a second positioning frame set of the image to be processed is determined according to an unsupervised target detection algorithm. Additionally, the method can include that a target positioning frame set in the image to be processed is determined according to the first positioning frame set and the second positioning frame set.

Abstract: Aspects of the present disclosure relate to a text labeling method and device, and more specifically to the field of deep learning and solving the problem of low efficiency and low accuracy of a feature extraction process. The method can include processing input information to obtain word embedding representation information of the input information, performing dynamic convolution feature extraction on the word embedding representation information to obtain a classification result of each character from the word embedding representation information, and inserting a label into the input information based on the classification result.

Abstract: The present disclosure provides a method and device for determining an optimal operation path. The method can include determining a model operation topology graph according to a neural network model, the model operation topology graph including a plurality of operation nodes, and selecting a plurality of operators with a same operation type as the operation node for each operation node in the model operation topology graph, and creating a corresponding operator node for each operator. Further, the method can include, according to a connection relationship between the operation nodes in the model operation topology graph, constructing an operator topology graph based on the created operator nodes, determining an operation index of each operator node, and weighting the operator topology graph based on the operation index of each operator node to obtain a weighted operator topology graph, and selecting an optimal operation path in the weighted operator topology graph.

Abstract: A method, apparatus, and non-transitory computer-readable storage medium for classification prediction are provided. The method for classification prediction includes obtaining a classification prediction request. The classification prediction request may include a branch identifier. The method for classification prediction may further include determining a service branch corresponding to the classification prediction request is determined from a started classification prediction service according to the branch identifier. The method for classification prediction may additionally include performing a classification prediction task based on the service branch.

Abstract: Aspects of the disclosure provide a responding method and device, an electronic device and a storage medium. The method is applied to a first electronic device including an audio acquisition component and an audio output component. The method can include acquiring a voice signal through the audio acquisition component, determining whether to respond to the voice signal, and responsive to determining to respond to the voice signal, outputting a first sound signal by the audio output component, the first sound signal being configured to notify at least one second electronic device that the first electronic device responds to the voice signal. In such a manner, an electronic device, responsive to determining to respond to a voice signal, outputs a sound signal to prevent other electronic device(s) from responding to the voice signal, so that competitions between electronic devices are reduced and a user experience is improved.

Abstract: A method and a storage medium are provided for training an image recognition model. An image recognition model is trained by using a tagged image sample set. Multiple to-be-tagged image samples in a to-be-tagged image sample set are recognized by using the image recognition model to obtain a confidence level of a recognition result corresponding to each of the multiple to-be-tagged image samples. At least one to-be-tagged image sample of which the confidence level is in a preset interval is selected as at least one to-be-tagged image sample to form a first to-be-tagged image sample set. Tags of to-be-tagged image samples in the first to-be-tagged image sample set is acquired. An updated tagged image sample set is obtained by updating the tagged image sample set according to the acquired tags. The image recognition model is trained by using the updated tagged image sample set.

Abstract: A method and device for processing information, and a storage medium is provided. The method is applied to an authorization proxy server, and includes receiving a first account information of a first vehicle-mounted terminal, determining, based on an associating record of a user account service, that a first account corresponding to the first account information is an authorized account that has been associated with the user account service, and authorizing the first account with a control right for controlling a device to be controlled.

Abstract: Aspects of the disclosure provide a method and device for information processing, a terminal, a server and a storage medium. The method can include that voice information is collected, and, responsive the voice information instructing a target application program to be started, instruction information configured to assist in starting the target application program is determined. The method can further include that multimedia information indicated by the instruction information is output according to the instruction information in a process of starting the target application program. Therefore, the multimedia information may be output in the process of starting the application program to improve the experience of a user in starting the application program.