BERT-based multi-feature fusion fuzzy text classification model

Abstract

The invention discloses a BERT-based multi-feature fusion fuzzy text classification model. The method comprises the following steps of preparing a fuzzy text classification original data set; a BERTMFFM model is constructed, the BERTMFFM model comprises a BERT model, a convolutional neural network, a bidirectional long-short memory network and a SelfAttention module, the input of the BERT model is a fuzzy text, the output of the BERT model is connected with the convolutional neural network, the bidirectional long-short memory network and the SelfAttention module, and local features, sentence semantic features and syntax structure features of the fuzzy text are extracted; splicing the output of the BERT model with the output of the bidirectional long-short memory network at the same time, and then screening out optimal sentence semantic features by using maximum pooling operation; and fusing the local features, the optimal sentence semantic features and the syntactic structure features by adopting a parallel splicing mode, and performing fuzzy text classification on a fusion result through a SoftMax function to finish the construction of the BERTMFFM model. The problem of incomplete feature collection is solved, so that the classification accuracy is improved.

Description

technical field [0001] The technical solution of the present invention relates to the technical field of natural language processing, specifically a BERT-based multi-feature fusion fuzzy text classification model. Background technique [0002] With the development of network technology, information has exploded, especially text data. The objective world contains a large amount of text information, such as journal documents, current news, emails, text messages, chat messages, and e-books. Due to the diversity and complexity of Chinese texts, fuzzy texts with overlapping content, similar content, high similarity between categories, and unclear boundaries have also increased in large numbers. In the mass of text information, how to efficiently manage and analyze a large amount of fuzzy text, and quickly obtain effective information from it has become an important task in the field of text classification. [0003] In text classification, text representation and feature extract...

AI Technical Summary

Problems solved by technology

[0006] In order to achieve a more accurate classification of fuzzy text and solve the problems of insufficient semantic understanding and incomplete feature acquisition in fuzzy text classification, this paper proposes a BERT-based multi-feature fusion fuzzy text classification model (Multi-feature Fusion Fuzzy Text Classification Model Based On BERT,BERT_MFFM)

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