在跨语言情绪分析中弥合资源缺口:通过数据增强和转移学习进行自适应自我调整
Li Chen1, Shifeng Shang2, Yawen Wang3
1Big Data Center, Huanghe Science and Technology College, Zhengzhou, China.
PeerJ. Computer science
|June 26, 2025
概括
本研究引入了一个适应性框架,以改善跨语言情绪分析,特别是对于资源较低的语言. 新方法显著提高了性能,缩小了高资源和低资源语言能力之间的差距.
科学领域:
- 自然语言处理自然语言处理.
- 计算语言学 计算语言学
- 机器学习 机器学习
背景情况:
- 跨语言情绪分析对于理解跨语言情绪至关重要.
- 在资源较少的语言 (低资源和中资源) 中存在显著的绩效差距.
- 现有的方法很难有效地解决资源不平衡问题.
研究的目的:
- 为大型语言模型 (LLM) 提出适应性自我调整框架.
- 为了减轻资源稀缺语言的跨语言情绪分析的绩效差异.
- 为多语言情绪分析建立一个新的基准.
主要方法:
- 开发了一种适应性自我调整框架,用于LLMs.
- 整合了新的数据增强技术.
- 利用转移学习策略来解决资源不平衡问题.
主要成果:
- 在11种语言中,F1平均得分提高了7.35分.
- 与最先进的基线相比,表现出卓越的性能.
- 在资源较少的语言中表现出卓越的有效性,缩小了绩效差距.
结论:
- 拟议的框架大大加强了跨语言情绪分析,特别是对于资源较低的语言.
- 该方法为现实应用提供了强大的域调整能力.
- 这项研究推进了包容和公平的自然语言处理解决方案.
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