外部知识增强的双编码器和对比学习,以获得方面情感的三重提取
Yuan Huang1, Xiaozheng Zhou1, Ruizhi Yin1
1School of Information and Electrical Engineering, Hebei University of Engineering, Handan, Hebei, China.
PloS one
|March 12, 2026
概括
本研究引入了一种新的方法,通过整合语义和语法信息来进行方面情感三重提取 (ASTE). 该方法增强了更深层次的句子理解,并改善了情感三胞胎的提取.
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
背景情况:
- 基于方面的情绪分析 (ABSA) 对于理解意见至关重要.
- 方面情感三元提取 (ASTE) 是ABSA内部的一个新兴子任务.
- 现有的ASTE方法往往无法捕捉到深层次的句子语义和单词含义.
研究的目的:
- 提出一个新的方面情感三重提取 (ASTE) 方法.
- 解决现有的ASTE方法在语义和语法信息挖掘方面的局限性.
- 改进提取方面术语,意见术语和情感极性.
主要方法:
- 使用的双编码器:一个语义编码器 (BERT和图形卷积网络 - GCN) 和一个语法编码器 (双向长期和短期存储器 - Bi-LSTM和GCN).
- 实现了一个功能融合模块,以整合语义和语法信息.
- 引入了边界意识的对比学习模块,以增强边界标签识别.
主要成果:
- 拟议的方法在ASTE-Data-V1和ASTE-Data-V2数据集上显示出有效性.
- 双编码器架构成功地提取了更深层次的语义和语法信息.
- 边界意识的对比学习模块改善了标签识别.
结论:
- 新的ASTE方法有效地整合了语义和语法信息,以改善情绪分析.
- 该方法增强了挖掘更深层次的句子语义和单独的单词含义.
- 拟议的技术显示出在推进基于方面的情感分析方面显著的前景.
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