通过集成的RNN自动编码器学习单词,句子,情感和段落表示的多视图文本分类
Yitao Ding1, Mohamed Shalaby2, Narinderjit Singh Sawaran Singh3
1School of Computer Science, Xijing University, Xi'an, 710123, Shaanxi, China.
Scientific reports
|December 11, 2025
概括
本研究介绍了用于文本分类的功能集成多视图RNN自动编码器 (FMV-RNN-AE). 该模型通过集成多个数据视图来增强性能,为复杂架构提供了一个内存高效的替代方案.
科学领域:
- 自然语言处理自然语言处理.
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 单视图文本分类方法通过通过单一镜头处理文档来限制性能.
- 捕捉文本信息的多维性质对于强大的文本分类至关重要.
研究的目的:
- 提出一个端到端的框架,FMV-RNN-AE,将多个文本视图集成在一起,以改进文本分类.
- 根据现有的单视图和多视图方法,评估拟议框架的有效性.
主要方法:
- FMV-RNN-AE框架整合了四种互补的文本视图:词级嵌入,句子级表示,基于情感的特征和段落级语义.
- 标准的RNN自动编码器用于学习压缩视图特定表示.
- 一个可学习的融合模块和联合优化用于分类,重点是原则集成.
主要成果:
- 在7个基准数据集中,FMV-RNN-AE表现出与单视图方法相比4.7%的持续改进,与现有的多视图方法相比2.2-4.0%的持续改进.
- 该框架在以情绪为导向的任务中取得了很高的准确性,包括93.5%的仇恨言论和92.7%的IMDb.
- 与BERT相比,FMV-RNN-AE使用的参数和内存要少得多,平均准确度可比.
结论:
- 拟议的FMV-RNN-AE框架为文本分类提供了一个内存高效和任务敏感的替代方案,特别是在耐延期的场景中.
- 精心设计的多视图自动编码器集成可以在受到限制的内存预算下,在各种各样的域中提高文本分类的稳定性.
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