Text feature extraction method and device, computer equipment and storage medium

Abstract

The invention relates to the technical field of data analysis, and discloses a text feature extraction method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining a text file, segmenting the text content of the text file through a word segmentation device, and obtaining a character sequence; performing feature extraction on the character sequence through N feature extraction layers of a pre-trained BERT model to obtain N feature vectors, wherein N is an integer greater than 1; and fusing the N feature vectors based on an attention mechanism to obtain a fusion vector, wherein the fusion vector is used for describing the text features of the text file. According to the method, the obtained N feature vectors are fused, and the N feature vectors can perform feature description on the text file from different angles, so that the obtained fusion vector can more comprehensively expropriate the text features of the text file, and the accuracy of the BERT model in a classification task is improved.

Description

technical field [0001] The present application relates to the technical field of data analysis, and in particular to a text feature extraction method, device, computer equipment and storage medium. Background technique [0002] At present, the application of natural language processing (NLP) has been popularized in various fields, and one of the main reasons for the popularity of natural language processing is due to the use of pre-trained models in natural language processing. To solve the problem that the natural language processing model needs a lot of training before use, the pre-trained model can adapt to perform different natural language processing operations on different data sets, without the need to build a model from scratch. [0003] Among the common pre-training models, the appearance of the BERT model significantly improves the accuracy of classification tasks in NLP, and the BERT model can be pre-trained through unlabeled sample files. However, in the existin...

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Problems solved by technology

[0004] This application provides a text feature extraction method, device, computer equipment, and storage medium, which solves the problem that the BERT model performs multi-dimensional feature extraction for each character in the text feature extraction process, ignoring the integrity of the text content, resulting in The technical problem that the accuracy rate of the classification result of the output text feature is reduced

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