在政治文本中识别因果归因
Identifying attributions of causality in political text
AI总结：
本文提出了一种用于检测和解析政治文本中因果解释的框架，通过训练轻量级因果语言模型，实现结构化数据集的生成，以提高对因果关系的分析能力。
摘要：
解释是人们理解政治世界的基本要素。公民经常询问并回答事件为何发生、谁该负责以及事情应如何不同等问题。然而，尽管其重要性，解释仍然是政治科学系统分析中发展不足的对象，现有方法碎片化且往往问题特定。我介绍了一个用于检测和解析政治文本中解释的框架。为此，我训练了一个轻量级的因果语言模型，该模型能够以因果关系对的形式返回结构化数据集的因果声明，用于后续分析。我展示了如何在大规模上研究因果解释，并展示了该方法相对人类编码的适度注释要求、通用性和准确性。
英文摘要：
Explanations are a fundamental element of how people make sense of the political world. Citizens routinely ask and answer questions about why events happen, who is responsible, and what could or should be done differently. Yet despite their importance, explanations remain an underdeveloped object of systematic analysis in political science, and existing approaches are fragmented and often issue-specific. I introduce a framework for detecting and parsing explanations in political text. To do this, I train a lightweight causal language model that returns a structured data set of causal claims in the form of cause-effect pairs for downstream analysis. I demonstrate how causal explanations can be studied at scale, and show the method's modest annotation requirements, generalizability, and accuracy relative to human coding.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
