Text classification method based on generative multi-task learning model

Abstract

The invention discloses a text classification method based on a generative multi-task learning model, and the method comprises the steps: alternately decoding a multi-task classification model througha training set according to a multi-label classification task and a hierarchical classification task, obtaining a current-moment semantic feature related to a coding moment feature vector through decoding, and carrying out the training of the current-moment semantic feature; optimizing the multi-task classification model according to the training result and the real label set to obtain an optimized multi-task classification model; inputting data in a to-be-detected set into the optimized multi-task classification model, and respectively obtaining classification labels to finish classification. The semantic association between the multi-label classification model and the hierarchical classification model is constructed through a multi-task mechanism in the training process of the model, sothat the semantic association between two sub-task prediction results can be enhanced, and the purpose of improving the sub-task classification performance is achieved.

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...

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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

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