Enhanced industrial text classification via hyper variational graph-guided global context integration.

ABSTRACT:

Background

Joint local context that is primarily processed by pre-trained models has emerged as a prevailing technique for text classification. Nevertheless, there are relatively few classification applications on small sample of industrial text datasets.

Methods

In this study, an approach of employing global enhanced context representation of the pre-trained model to classify industrial domain text is proposed. To achieve the application of the proposed technique, we extract primary text representations and local context information as embeddings by leveraging the BERT pre-trained model. Moreover, we create a text information entropy matrix through statistical computation, which fuses features to construct the matrix. Subsequently, we adopt BERT embedding and hyper variatio

SUBMITTER: Zhang G 

PROVIDER: S-EPMC10773843 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature