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食品商店的名字是什么? 利用大型语言模型来增强食品环境数据

Analee J Etheredge¹, Samuel Hosmer¹, Aldo Crossa¹

¹Center for Population Health Data Science, NYC Department of Health and Mental Hygiene, New York City, NY, United States.

Frontiers in artificial intelligence

|December 23, 2024

概括

使用大型语言模型 (LLM) 进行食品环境分类的自动化显著提高了卫生部门的效率. 这种自然语言处理方法根据名称对食品商店进行分类,克服了用于公共卫生研究的手动数据审查的局限性.

科学领域:

公共卫生信息学公共卫生信息学
计算语言学计算语言学
城市规划城市规划

背景情况:

行政食品数据通常是无结构的,这阻碍了其用于公共卫生洞察力的使用.
手动对食品环境进行分类是耗时且资源密集的.
自然语言处理 (NLP) 和深度学习为数据分类提供了潜在的解决方案.

研究的目的:

使用基础模型 (BERT) 演示食品商店的自动分类.
开发一个以公共卫生为导向的食品零售分类系统.
为了展示LLMs在食品环境计数中的效率提升.

主要方法:

开发了10个食品零售类别用于公共卫生分类.
使用BERT,一个大型语言模型 (LLM),用于自动化商店名称分类.
在纽约市的一家门店目录上调整并评估了该模型,F1得分为0.710 (微) 和0.709 (宏).

主要成果:

该LLM成功地根据名称对食品商店进行了分类,复制了已知的时空模式.
证明了模型的实用性,作为餐厅数据集上的数据质量增强工具.
与传统的手工方法相比,实现了显著的效率提高.

结论:

法律法规提供了一种新且高效的方法来列举食品环境.
自动化分类解决了当地卫生部门的关键运营挑战.
这种方法提高了行政食品数据的可用性,用于公共卫生研究和行动.

关键词:

食品行政数据食品行政数据深度学习是一种深度学习.食品环境分类食品环境分类食品商店名称食品商店名称卫生部门卫生部门卫生部门大型语言模型.机器学习是机器学习.自然语言处理自然语言处理.

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