A Cross-Domain Text Classification Method Based on Adaptive Noise Reduction Encoder

Abstract

The invention discloses a cross-domain text classification method based on an adaptive noise reduction encoder, which is characterized in that: a feature selection method suitable for cross-domain tasks is used to filter samples in the source domain data set and the target domain data set Feature words with low frequency and meaningless appearing in , and adaptively calculate the optimal noise interference coefficient according to the distribution difference between the samples in the source domain set and the target domain set, and use the optimal noise interference coefficient to analyze the feature space For perturbation, a new feature space is built using the stacked edge denoising encoder approach and a classifier is constructed. The invention can better excavate the relationship between latent features among fields, reduce field differences, and thus can improve the correctness of classification.

Description

technical field [0001] The invention relates to a cross-domain text classification method based on an adaptive noise reduction encoder, and classifies network text data information, more specifically, cross-domain classification for network text data information in different fields and different data distributions . Background technique [0002] In recent years, with the rapid rise of online social platforms such as blogs, WeChat, and Weibo, a large amount of text information has been generated on the Internet. These massive data often contain huge potential commercial value. Information can be used to improve or upgrade products in a targeted manner, so as to meet consumer needs and increase market competitiveness; It will be more favored by consumers. In view of this, research in related fields such as text classification has extremely important value and significance. [0003] However, because the data in the network is affected by multiple factors such as users and ti...

AI Technical Summary

Problems solved by technology

Cross-domain classification tasks need to process features of multiple different domains at the same time, which further enlarges the feature space and further exacerbates the high-dimensional and sparse nature of text data, which makes it difficult to select meaningful features and provides a common way for learning. Feature spaces for cross-domain classification pose challenges

[0007] Second, although the edge denoising encoder can learn a relatively robust feature space in cross-domain classification tasks, however, its learning results are sensitive to noise coefficients

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