基于微调BERT的论文分类推荐系统研究

2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2023-08-01 DOI:10.1109/IRI58017.2023.00058

Dipto Biswas, Joon-Min Gil

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引用次数: 0

摘要

在本文中，我们比较了两种流行的NLP模型，预训练微调BERT和BiLSTM与组合CNN在研究论文分类和推荐任务方面的性能。我们使用研究期刊基准数据集对这两种模型进行了性能评估。性能结果表明，预训练微调BERT模型在分类性能上优于CNN-BiLSTM组合模型。

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Research Paper Classification and Recommendation System based-on Fine-Tuning BERT

In this paper, we compare the performance of two popular NLP models, pre-train fine-tuned BERT and BiLSTM with combined CNN, in terms of the classification and recommendation tasks of research papers. We conduct the performance evaluation of these two models with research journal benchmark dataset. Performance results show that the pre-train fine-tuned BERT model is superior to CNN-BiLSTM combined model in terms of classification performance.

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来源期刊

2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI)

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