Text classification problems via BERT embedding method and graph convolutional neural network
Computation and Language
2022-09-07 v3 Machine Learning
Machine Learning
Abstract
This paper presents the novel way combining the BERT embedding method and the graph convolutional neural network. This combination is employed to solve the text classification problem. Initially, we apply the BERT embedding method to the texts (in the BBC news dataset and the IMDB movie reviews dataset) in order to transform all the texts to numerical vector. Then, the graph convolutional neural network will be applied to these numerical vectors to classify these texts into their ap-propriate classes/labels. Experiments show that the performance of the graph convolutional neural network model is better than the perfor-mances of the combination of the BERT embedding method with clas-sical machine learning models.
Keywords
Cite
@article{arxiv.2111.15379,
title = {Text classification problems via BERT embedding method and graph convolutional neural network},
author = {Loc Hoang Tran and Tuan Tran and An Mai},
journal= {arXiv preprint arXiv:2111.15379},
year = {2022}
}