Text Classification Research Based on Bert Model and Bayesian Network
Songsong Liu, Haijun Tao, Shiling Feng · 2019
The Bert model is a pre-training model based on deep learning. It has refreshed the best performance of 11 NLP missions as soon as it appears, and it also has a wide range of applications. The text data of people's livelihood governance is huge, and there is a large amount of unstructured data, which makes the traditional text analysis and mining technology increase in the space-time complexity of computing; so it is very important to choose appropriate text classification technology. Here we propose to use the Bert model in combination with the Bayesian network to achieve one new classification of text. That is, the Bayesian network is used to perform the classification of two categories first, and we can get the approximate category range of each text, and then the Bert model is used to classify the text into specific categories. The combination of these two methods can greatly reduce the errors caused by the classification defects of using only one of the methods. Thereby achieving an improvement in the accuracy of text classification.