Text classification method based on semi-supervised transfer learning

Abstract

The invention discloses a text classification method based on semi-supervised transfer learning. The method comprises the following steps of: (1) data set and data preprocessing: acquiring a small amount of marked data sets and a large amount of unmarked data sets, cleaning and denoising, vectorizing data set samples through a word2vec method, and selecting 100 vector dimensions; (2) data enhancement: performing K times of text enhancement on each sample in the unmarked data in an anti-translation mode; (3) pseudo label pre-judging: inputting a labeled sample into a pre-training model Bert, and carrying out model migration by adopting a fine tuning method; (4) mixing samples; and (5) text classification: using the trained best model for carrying out text classification prediction on data in a test set. According to the text classification method based on semi-supervised transfer learning, semi-supervised learning is combined, transfer learning is used for solving the problem that annotation data are difficult to obtain in the field of text classification, and meanwhile the performance of a text classification model can be improved.

Description

technical field [0001] The invention relates to the technical field of text classification, in particular to a text classification method based on semi-supervised transfer learning. Background technique [0002] Deep learning is an important method in the field of natural language processing. It can achieve good performance in tasks such as text classification, entity recognition, machine translation, and sentiment analysis. However, deep learning has a fundamental weakness: it requires a large amount of labeled data to work properly. . However, in many fields in the real world, there is often the difficulty of insufficient labeled data, and labeling a large amount of unlabeled data is a very expensive task. Therefore, how to reduce the demand of deep learning models for the amount of labeled sample data while improving the predictive performance of the model is an important research content. Contents of the invention [0003] The purpose of the present invention is to p...

AI Technical Summary

Problems solved by technology

[0002] Deep learning is an important method in the field of natural language processing. It can achieve good performance in tasks such as text classification, entity recognition, machine translation, and sentiment analysis. However, deep learning has a fundamental weakness: it requires a large amount of labeled data to work properly.

However, in many fields in the real world, there is often the difficulty of insufficient labeled data, and labeling a large amount of unlabeled data is a very expensive task.

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