| Popis: |
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. |
