相关实验视频
TurkSentGraphExp:来自预先训练的LLM的内在图表意识可解释性框架,用于土耳其情绪分析
Yasir Kilic1, Cagatay Neftali Tulu2
1Computer Engineering Department, Adana Alparslan Turkes Science and Technology University, Adana, Turkey.
PeerJ. Computer science
|March 26, 2025
概括
本研究介绍了TurkSentGraphExp,这是一个新的图表意识可解释性解决方案,用于土耳其情绪分析. 它通过在聚合性土耳其文本中捕捉复杂的语义关系来增强模型的解释性.
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:
- 情绪分类对于客户反分析和社交媒体监控等应用至关重要.
- 现有的解决方案往往依赖于黑子模型,限制了可解释性,特别是对于像土耳其语这样的聚合性语言.
- 土耳其NLP当前的可解释性方法往往无法捕捉复杂的词汇和语义关系.
研究的目的:
- 为土耳其情绪分析提出一个图表意识的可解释性解决方案.
- 解决黑盒模型的局限性,提高情绪分类的可解释性.
- 在情感分析中有效地处理土耳其语的聚合性.
主要方法:
- 开发TurkSentGraphExp,这是一个图表意识的解释性框架,用于分析土耳其情绪.
- 使用图形表示学习 (GRL) 来捕捉土耳其文本中的语义结构和关系.
- 考虑到后的语义结构和土耳其语的聚合性.
主要成果:
- 与最先进的方法相比,TurkSentGraphExp在可解释性方面取得了10-40%的改善.
- 该框架在不同的稀疏度水平上表现出强度.
- 案例研究证实了该模型从土耳其文本中的附加词中识别语义关系的能力.
结论:
- 通过利用图形表示,TurkSentGraphExp为土耳其情绪分析提供了更好的解释性.
- 该解决方案有效地捕捉了土耳其语的复杂聚合结构,提供了合理的短语级别可解释性.
- 这项工作通过整合语义和结构信息,在土耳其NLP中推进了可解释AI.
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