CNN:研究不同情况下对中国新闻头条的分类方法
1College of Literature and Journalism, Xiangtan University, Chongwen Rd, Yuhu District, Xiangtan, 411105, Hunan, China. yyan_cn@163.com.
Scientific reports
|August 8, 2025
概括
这项研究引入了两个新型模型,ERNIE-AAFF-SECNN和ERNIE-MSSE-DSCNN,以改善中国新闻头条的分类. 这些模型有效地解决了诸如特征稀缺性和数据稀缺性等挑战,大大提高了分类准确性.
科学领域:
- 自然语言处理自然语言处理.
- 机器学习 机器学习
- 信息检索 信息检索
背景情况:
- 互联网时代导致了大量的文本数据增长,增加了对高效新闻传播和分类的需求.
- 极短的中国新闻标题带来了挑战,因为信息有限,特征稀疏和模两可.
- 现有的方法与短文数据的独特特征作斗争,需要先进的分类方法.
研究的目的:
- 开发有效的深度学习模型来对极短的中国新闻标题进行分类.
- 解决大规模和小规模数据集中的特征稀缺性和数据稀缺性问题.
- 提高新闻标题分类系统的准确性和稳定性.
主要方法:
- 对于大规模数据集,开发了一种改进的卷积分类模型 (ERNIE-AAFF-SECNN),其中包括来自ERNIE变压器层的自适应特征融合,全球背景的BiLSTM,TextCNN的SE关注以及ReLU激活.
- 对于小规模数据集,构建了一个深度可分离的卷积分类模型 (ERNIE-MSSE-DSCNN),结合了AEDA数据增强,深度可分离的卷积,多级SE注意力机制和FGM对抗训练.
- 这两种模型都利用注意力机制和先进的卷积技术来捕捉深层次的语义和局部特征.
主要成果:
- 根据ERNIE-AAFF-SECNN模型,中国大规模新闻标题分类的准确性得到了显著改善.
- ERNIE-MSSE-DSCNN模型在小规模数据集上取得了显著的性能增长,克服了数据限制.
- 对各种数据集的实验结果证实了拟议模型在现有方法上的优越性.
结论:
- 拟议的ERNIE-AAFF-SECNN和ERNIE-MSSE-DSCNN模型为中国新闻头条分类提供了有效的解决方案,处理大型和小型数据集.
- 适应性特征融合,多尺度注意力和对抗性培训是提高简短,模两可的文本分类性能的关键组成部分.
- 这些进步有助于在大数据时代更有效,更准确的信息检索和组织.
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