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语法解读和多策略辅助增强以面向为基础的情绪分析

Lu Liu¹, Da Li¹, Chuanxu Yue¹

¹Institute of Automation, Qilu University of Technology (Shandong Academy of Sciences), JiNan, ShanDong, China.

|August 12, 2025

概括

本研究引入了用多策略辅助增强 (SDMAE) 进行语法解读,通过改进依赖树和整合语义和语法数据以更准确地检测情绪来改进基于方面的情绪分析 (ABSA).

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
机器学习机器学习

背景情况:

基于方面的情感分析 (ABSA) 传统上依赖于依赖树的语法特征.
在线评论中的杂或不规则的句子结构阻碍了基于语法的图形卷积网络 (GCN) 模型的性能.
语法和语义信息的整合不足限制了现有的ABSA方法中的情绪检测准确性.

研究的目的:

提出一种新的方法,即用多策略辅助增强 (SDMAE) 进行语法解读,以克服ABSA的局限性.
为了提高情绪极性检测对特定方面术语的准确性.
改进语法和语义信息的整合,以提高ABSA的性能.

主要方法:

修剪依赖树,专注于情感关键的语音部分特征,以减少噪音.
实现一个多通道自适应聚合模块 (MAAM),以多头关注融合语义和语法表示.
采用多策略任务学习框架,使用情感词典和监督对比学习.

主要成果:

拟议的SDMAE方法显著改善了四个基准数据集的性能.
与基于方面的情绪分析中的几种最先进的方法相比,表现出优异的结果.
在依赖性解析中有效的降噪和增强的功能集成是性能增长的关键.

结论:

SDMAE为基于方面的情绪分析提供了强大的解决方案,特别是对于杂的文本数据.
无效的语法信息和语义特征的协同整合导致了显著的性能改进.
开发的方法为未来的情感分析和自然语言理解研究提供了有希望的方向.

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