捕获因果关系声明:一个微调的文本挖掘模型,用于从社会科学论文中提取因果关系句子
Rasoul Norouzi1, Bennett Kleinberg1,2, Jeroen K Vermunt1
1Methodology and Statistics, Tilburg University, Tilburg, Netherlands.
Research synthesis methods
|February 2, 2026
概括
这项研究开发了一个文本挖掘模型,自动从社会科学研究中提取因果句子. 特定域微调显著提高了准确性,有助于因果关系索赔分析.
科学领域:
- 社会科学 社会科学 社会科学
- 计算语言学 计算语言学
- 文本挖掘 (Text Mining) 是一个很好的方法.
背景情况:
- 在社会科学中,因果关系对于理论和实践至关重要.
- 社会科学文学中的模两可的语言阻碍了明确的因果讨论.
- 需要自动化方法来识别大文本大体中的因果关系声明.
研究的目的:
- 引入一个文本挖掘模型,从社会科学论文中提取因果句子.
- 评估特定领域微调对模型性能的影响.
- 为了使社会科学研究中因果关系主张的大规模分析.
主要方法:
- 从合作数据库 (CoDa) 整理了一套由1058个手动注释的句子 (529个因果,529个非因果) 组成的数据集.
- 在社会科学和通用因果关系数据集上微调了几个基于变压器的语言模型 (BERT,SciBERT,RoBERTa,LLAMA,Mistral).
- 在社会科学和通用测试集上评估模型性能.
主要成果:
- 在社会科学数据集上的微调变压器模型显著提高了因果句子提取精度.
- 域特定微调提高了性能,即使有有限的注释数据.
- 仅在通用数据上微调的模型在社会科学文本上表现较差.
结论:
- 特定于领域的微调和数据对于准确地捕捉学术写作中的因果语言至关重要.
- 自动化因果句子提取方法促进了社会科学中因果索赔的全面分析.
- 这种方法支持揭示社会现象机制,理论发展和方法论的严谨性.
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