基于粒子群优化的NLP方法,用于优化自动文件分类和检索
Bowen Zeng1, Xianhe Shang1, Rong Lu1
1CNNP Nuclear Power Operations Management Co., Ltd., Jiaxing, China.
PloS one
|July 2, 2025
概括
新的PBX模型通过整合BERT和ConvXGB来提高文本分类的准确性,并通过粒子群优化 (PSO) 进行了优化. 这种方法显著提高了多类任务和复杂文档的性能.
科学领域:
- 自然语言处理自然语言处理.
- 机器学习 机器学习
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 文本分类对于NLP任务,如情绪分析和信息检索至关重要.
- 现有的模型面临着多类分类和复杂文档的挑战.
研究的目的:
- 引入PBX模型,这种混合方法结合了深度学习和传统机器学习,以改进文本分类.
- 通过BERT预训练,ConvXGB分类和粒子群优化 (PSO) 来提高模型性能.
主要方法:
- 利用BERT进行基于深度学习的文字预训练.
- 使用ConvXGB模块进行文本分类.
- 应用粒子优化 (PSO) 用于超参数优化.
- 在各种数据集上评估模型:20个新闻组,路透社-21578和AG新闻.
主要成果:
- 该PBX模型在准确性,精度,回忆和F1分数方面表现优于现有方法.
- 在AG新闻数据集上获得了95.0%的准确性和94.9%的F1分数.
- 废弃性研究证实了PSO,BERT和ConvXGB的显著贡献.
结论:
- 该PBX模型为具有挑战性的文本分类任务提供了强大的解决方案.
- 未来的研究将涉及较小类别的性能和更广泛的应用范围.
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