支持工作生活曝光率:为增强文献搜索,注释职业曝光率
Paul Thompson1, Sophia Ananiadou1, Ioannis Basinas2
1Department of Computer Science, National Centre for Text Mining, University of Manchester, Manchester, United Kingdom.
PloS one
|August 15, 2024
概括
我们创建了一个新的科学文章数据集,以训练自然语言处理模型来识别职业物质暴露. 这将有助于建立更好的工具来更新工作场所的暴露信息.
科学领域:
- 职业健康 职业健康 职业健康
- 自然语言处理自然语言处理.
- 生物信息学是一种生物信息学.
背景情况:
- 工作场所的暴露显著影响非传染性疾病的风险.
- 职业暴露矩阵 (JEM) 估计了职业暴露,但其范围和更新频率有限.
- 科学文献是丰富的暴露数据来源,但由于其体积,难以处理.
研究的目的:
- 为训练机器学习模型在命名实体识别 (NER) 中开发一个新的注释体.
- 通过自然语言处理 (NLP) 支持有效开发和更新工作暴露矩阵.
- 改进科学文献中职业物质暴露的识别.
主要方法:
- 开发了一系列科学文章的新注释体,专注于职业物质暴露.
- 利用了专家注释者,并完善了注释过程,以确保高度一致.
- 使用语料库训练和评估用于命名实体识别 (NER) 的机器学习模型.
主要成果:
- 通过增量改进,在专家注释者之间达到了高度共识.
- 证明了该集体训练高性能NER模型的能力.
- 在职业暴露研究中建立了NLP工具开发的基础.
结论:
- 开发的注释体对于在职业健康领域推进NLP应用至关重要.
- 该资源将简化创建和更新工作暴露矩阵.
- 促进对工作场所物质暴露的更有效的研究.
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