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通过使用手套和ResNeXt进行关键词提取,并通过增强的人类进化优化 (EHEO) 算法进行优化
Chao Pan1, Yanshu Liu2, Mohammad Sarabi3,4
1Hunan Mass Media Vocational and Technical College, Changsha, 410100, Hunan, China.
Scientific reports
|November 25, 2025
概括
本研究介绍了一种使用ResNeXt和进化优化的高级关键词提取方法,实现高精度和回忆,用于文档总结和信息检索.
科学领域:
- 自然语言处理自然语言处理.
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 关键词提取 (KPE) 对于文档总结,搜索引擎优化和信息检索至关重要.
- 手动的KPE是耗时的,自动化的方法与上下文理解相扎.
- 现有的KPE自动化技术需要提高准确性和效率.
研究的目的:
- 开发一种创新且有效的自动化关键词提取方法.
- 为了增强对文本中的上下文关系的理解,为KPE.
- 为各种领域提供可用于自动提取关键词短语的强大解决方案.
主要方法:
- 采用了ResNeXt神经网络架构.
- 使用增强的人类进化优化算法优化模型.
- 集成的GloVe-100词嵌入用于上下文表示.
- 在KP20k,Inspec和SemEval-2010数据集上评估模型.
主要成果:
- 与BERT和CNN-BERT等先进方法相比,实现了卓越的性能.
- 在KP20k数据集上实现了98.81%,98.67%和98.74%的回忆,精度和F1分数值.
- 在Inspec (96.54%精度,96.32%回忆,96.43%F1分) 和SemEval-2010 (97.32%精度,97.81%回忆,97.56%F1分) 上表现出色.
结论:
- 高质量的嵌入和优化策略与先进的神经架构的整合对KPE.是有效的.
- 拟议的模型提供了一个强大而高效的解决方案,用于自动提取关键词短语.
- 该方法显示了在需要文本分析的不同领域的广泛应用的潜力.
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