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A Multiscale Interactive Attention Short Text Classification Model Based on BERT
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DOI：10.1109/ACCESS.2024.3478781.png)
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Abstract
En 中文
Text classification tasks aim to comprehend and classify text content into specific classifications. This task is crucial for interpreting unstructured text, making it a foundational task in the field of Natural Language Processing(NLP). Despite advancements in large language models, lightweight text classification via these models still demands substantial computational resources. Therefore, this paper presents a multiscale interactive attention short text classification model based on BERT, which is designed to address the short text classification problem with limited resources. A corpus containing news articles, Chinese comments, and English sentiment classifications is employed for text classification. The model uses BERT pre-trained word vectors as embedding layers, connects to a multilevel feature extraction network, and further extracts contextual features after feature fusion. The experimental results on the THUCNews, Today's headline news corpus, the SST-2 dataset, and the Touhou 38 W dataset demonstrate that our method outperforms all existing algorithms in the literature.
Keywords:
BERT
RNN
CNN
CNN
multiscale interactive attention
multiscale interactive attention
pre-training models
pre-training models
multiscale interactive attention
pre-training models