双图形卷积网络集成情感知识和位置信息,用于方面情感三重提取
Yanbo Li1, Qing He1, Damin Zhang1
1College of Big Data and Information Engineering, Guizhou University, Guiyang, China.
Frontiers in neurorobotics
|September 4, 2023
概括
本研究介绍了双图卷积网络,将情感知识和位置信息 (DGCNAP) 集成为方面情感三重提取 (ASTE). 该模型通过结合情感知识和位置数据来增强从评论中提取信息.
科学领域:
- 自然语言处理 (NLP) 是一种自然语言处理.
- 人工智能 (AI) 是一种人工智能.
- 机器学习 (ML) 是指机器学习.
背景情况:
- 视角情感三元提取 (ASTE) 对于分析消费者反至关重要.
- 现有的模型往往忽视了情感知识和三位一体之间的关系.
- 需要先进的NLP模型来捕捉微妙的情绪信息.
研究的目的:
- 为ASTE提出一个新的端到端模型,DGCNAP.
- 将情感知识和位置信息集成到ASTE模型中.
- 改进从文本中提取方面 - 意见 - 情感三重体.
主要方法:
- 开发了双图卷积网络,整合了情感知识和位置信息 (DGCNAP).
- 将SenticNet的情感知识融入到依赖图形构建中.
- 在GCN中使用多目标位置感知功能来权衡单词接近.
主要成果:
- 在ASTE-Data-V2数据集上,DGCNAP显著超过了最先进的模型.
- 获得了高的F1成绩:70.72 (14分),57.57 (14圈),61.19 (15分) 和69.58 (16分).
- 证明了整合情感和位置信息的有效性.
结论:
- 拟议的DGCNAP模型在方面情感三位数提取方面提供了重大进展.
- 整合情感知识和位置信息可以提高情绪分析的准确性.
- 这种方法可以更有效地从评论中提取信息,从而改善产品和服务.
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