测量人际枪支暴力:自然语言处理方法,以解决刑事指控数据的局限性
Julie M Kafka1,2, Julia P Schleimer1,3, Ott Toomet4
1Firearm Injury & Policy Research Program, University of Washington, Seattle, WA 98195, United States.
概括
自然语言处理 (NLP) 准确地识别了暴力犯罪的法庭记录中的枪支参与. 这种方法为研究枪支暴力提供了一种可行且节省时间的方法.
科学领域:
- 公共卫生 公共卫生
- 计算机科学 计算机科学
- 犯罪学 犯罪学
背景情况:
- 枪支暴力是美国重要的公共卫生问题.
- 现有的数据基础设施不足以进行全面的研究.
- 法庭记录包含有关暴力犯罪的宝贵信息.
研究的目的:
- 开发和评估用于对法庭文件进行分类的自然语言处理 (NLP) 方法.
- 用NLP识别涉及枪支参与涉嫌暴力犯罪的情况.
- 评估NLP对此任务的可行性和准确性.
主要方法:
- 数字化和审查了华盛顿州的1472份法庭记录.
- 开发了一个关键词搜索和训练监督机器学习分类器.
- 使用持有测试集评估分类器性能.
主要成果:
- 一个决策树分类器取得了最佳表现,F1得分为0.82.2.
- 一个关键词列表显示了完美的回忆 (1.0),但F1得分较低 (0.65).
结论:
- 在法庭文件中,NLP是一种准确和可行的方法来识别枪支参与.
- 这种方法有可能在数据分析中节省大量时间.
- 这些发现支持NLP用于研究枪支暴力的使用.
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