Combined Method Based on Source Text and Representation for Text Enhancement

Abstract

Text classification is a basic and important work in natural language processing (NLP). The existing text classification models are powerful. However, training such a model requires a large number of labeled training sets, but in the actual scene, insufficient data is often faced with. The lack of data is mainly divided into two categories: cold start and low resources. To solve this problem, text enhancement methods are usually used. In this paper, the source text enhancement and representation enhancement are combined to improve the enhancement effect. Five sets of experiments are designed to verify that our method is effective on different data sets and different classifiers. The simulation results show that the accuracy is improved and the generalization ability of the classifier is enhanced to some extent. We also find that the enhancement factor and the size of the training data set are not positively related to the enhancement effect. Therefore, the enhancement factor needs to be selected according to the characteristics of the data.