改进基于跨度的方面情感三元提取,使用部分语音过和对比学习
Qingling Li1, Wushao Wen1, Jinghui Qin2
1School of Computer Science and Engineering, Sun Yat-Sen University, Guangzhou, China.
概括
本研究引入了一种新的跨度层次方法,用于使用语法知识来提高准确度的方面情感三重提取 (ASTE). 该方法增强了模型训练,并在提取情绪三重体中取得了最先进的结果.
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
- 计算语言学 计算语言学
背景情况:
- 方面情绪三重提取 (ASTE) 是细粒度情绪分析的一个关键任务.
- 现有的ASTE方法由于在列举所有可能性时产生的杂的候选跨度而难以实现性能.
- 这种噪音使模型训练和预测变得复杂,导致结果低于最佳.
研究的目的:
- 为了开发一个更有效和高效的方法,方面情感三重提取.
- 提高ASTE模型的准确性和降低其复杂性.
- 提高各方面和意见的跨度层次表示的质量.
主要方法:
- 一种新的跨度层次方法,结合语法知识 (部分语音过) 来生成精确的候选跨度.
- 将上下文嵌入式集成到跨度级别表示中,以提高质量.
- 一个辅助的对比性学习损失,以创建更紧的表示相同极性情绪.
主要成果:
- 拟议的方法显著减少了候选跨度中的噪音,促进了更容易的模型培训.
- 改进的跨度级别表示导致更好的情绪关系预测.
- 在基准数据集 (14Lap, 14Res, 15Res, 16Res) 上实现基于跨度的三重提取的最先进的性能.
结论:
- 新的跨度层次方法有效地解决了以前的ASTE方法的局限性.
- 语法知识和对比学习提高ASTE模型的性能.
- 该模型在准确提取情绪信息方面表现出卓越的有效性.
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