Abstract: Implementations of the present specification provide a method and an apparatus for training a service model, and a method and an apparatus for determining a text classification category. During specific implementation, on the one hand, text is processed by using an encoding network to obtain a corresponding semantic vector; on the other hand, a relationship network is established for classification categories based on a hierarchical relationship, and the relationship network is processed by using a graph convolutional network, to fuse information of nodes to obtain category expression vectors. Then, the semantic vector of the text is fused with the category expression vectors to determine a prediction result of a classification category. In a phase of training a service model, the prediction result can be compared with a sample label to determine a loss and adjust model parameters.

Abstract: Implementations of the present specification provide a method and an apparatus for training a service model, and a method and an apparatus for determining a text classification category. During specific implementation, on the one hand, text is processed by using an encoding network to obtain a corresponding semantic vector; on the other hand, a relationship network is established for classification categories based on a hierarchical relationship, and the relationship network is processed by using a graph convolutional network, to fuse information of nodes to obtain category expression vectors. Then, the semantic vector of the text is fused with the category expression vectors to determine a prediction result of a classification category. In a phase of training a service model, the prediction result can be compared with a sample label to determine a loss and adjust model parameters.