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Updated: Jul 26, 2025

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H4H:为无家可归者提供住房资源的全面存储库
Samue Osebe1, Jack Tsai2, Yu Hong1
1University of Massachusetts Amherst.
这项研究创建了无家可归者住房 (H4H),这是一个关于紧急庇护所的大数据集. 自然语言处理被用来提取无家可归的人的重要信息,改善了资源的可访问性.
科学领域:
- 计算机科学 计算机科学
- 社会科学 社会科学 社会科学
- 公共卫生 公共卫生
背景情况:
- 在美国,在任何一天晚上,超过50万个人经历无家可归.
- 大约50%的无家可归者使用庇护所,这表明他们对可访问信息的需求尚未得到满足.
- 现有的无家可归人口资源往往缺乏全面的,容易获取的信息.
研究的目的:
- 开发无家可归者住房 (H4H),这是最大的紧急庇护所和住房资源库.
- 采用先进的自然语言处理 (NLP) 来提取无家可归的人的关键信息.
- 创建一个基准数据集,用于未来的社会服务信息提取研究.
主要方法:
- 信息提取作为一个问答任务.
- 利用最先进的NLP模型,包括两步分类和带提示的问答罗伯塔模型.
- 通过使用 2,055 个问答对来训练和评估模型.
主要成果:
- 表现最好的NLP模型获得了75.83.83的宏观平均F1得分.
- 成功提取了重要的信息,如入学流程,提供的服务,逗留时间和资格标准.
- 无家可归者住房 (H4H) 数据集和注释条目现在是公开的.
结论:
- 开发的NLP方法有效地从庇护所数据中提取关键信息.
- H4H数据集是无家可归者和研究人员的宝贵资源.
- 这项工作促进了弱势群体获得重要信息的可访问性.
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