A text classification method based on a generative multi-task learning model

Abstract

The present invention constructs a text classification method based on a generative multi-task learning model, and alternately decodes the multi-task classification model according to the multi-label classification task and hierarchical classification task through the training set, and obtains the current Time semantic features and training; optimize the multi-task classification model according to the training results and the real label set to obtain the optimized multi-task classification model; input the data in the test set into the above-mentioned optimized multi-task classification model, and obtain classification labels respectively to complete the classification ; During the training process, the above model constructs the semantic correlation between the multi-label classification model and the hierarchical classification model through the multi-task mechanism, so the semantic correlation between the prediction results of the two sub-tasks can be enhanced, so as to achieve the purpose of improving the classification performance of the sub-tasks .

Description

technical field [0001] The invention belongs to the technical field of natural language processing text classification, and in particular relates to a text classification method based on a generative multi-task learning model. Background technique [0002] Text multi-label classification and hierarchical classification are problems that often need to be solved in practical applications. They are also two important branches of text classification tasks and current research hotspots. In practical applications, many data are ambiguous, and an instance may correspond to multiple category labels in the label set. The purpose of text multi-label classification is to establish a one-to-many association between text and label set. Compared with traditional single-label classification, multi-label classification is a method that is more in line with real application scenarios. For example, in the public security business scenario, the process of case acceptance will generate a large...

AI Technical Summary

Problems solved by technology

[0004] The present invention proposes a text classification method based on a generative multi-task learning model, which is used to overcome the defect that the classification effect is difficult to be comprehensively optimized due to the lack of semantic association in various classification models in the prior art. Label classification tasks and hierarchical classification tasks are integrated into a multi-task classification model to achieve the purpose of improving sub-task classification performance

Images