Chinese text classification method based on BERT and CNN hierarchical connection

Abstract

The invention relates to a Chinese text classification method based on BERT and CNN hierarchical connection. The method is mainly used for solving the text classification problems of sentiment analysis, core sentence recognition, relation recognition and the like of Chinese texts. According to the method, hierarchical connection is carried out by using a CNN model and a BERT model to obtain a newmodel BERT-CNN. Due to the fact that the BERT-CNN model is added, sentence features extracted by the BERT model can be further extracted, and more effective sentence semantic representation is obtained. Therefore, in the text classification task, a better classification effect can be obtained.

Description

technical field [0001] The invention belongs to the technical field of natural language processing, in particular to a Chinese text classification method based on deep learning model BERT and CNN hierarchical connection. Background technique [0002] With the rapid development of the economy and the Internet, more and more people will choose to express various opinions online. Faced with a large amount of text data on the Internet, how to efficiently obtain valuable data from these data has become a research hotspot. Question-answering robots, search, machine translation, and sentiment analysis are all key application areas of natural language processing, and these technologies are inseparable from text classification technology, which is the basis of these technologies. It is precisely because text classification technology is a foundation that the accuracy requirements for it are relatively high. Therefore, over the years, text classification technology has been a resear...

AI Technical Summary

Problems solved by technology

Traditional deep learning models rely on quantifying words or words in sentences as model input, but this method is sometimes affected by quan

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