探索COVID相关的关系提取:对比数据源和分析错误信息
Tanvi Sharma1, Amer Farea1, Nadeesha Perera1
1Predictive Society and Data Analytics Lab, Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland.
Heliyon
|March 8, 2024
概括
变压器语言模型有效地从PubMed和Reddit中提取COVID-19信息. 这些模型可以识别新的关系并检测错误信息,突出Reddit.
科学领域:
- 计算语言学计算语言学
- 医疗信息学医学信息学
- 公共卫生 公共卫生
背景情况:
- 由于COVID-19的流行,医疗保健系统需要快速获取知识.
- 文本数据是提取关键医疗和生物信息的未充分利用的资源.
- 需要有效的方法来处理大量的生物医学文本.
研究的目的:
- 使用基于变压器的语言模型提取COVID-19相关的关系.
- 为了比较像BERT和DistilBERT这样的模型在PubMed和Reddit数据上的表现.
- 评估模型检测新型关系和错误信息的能力.
主要方法:
- 利用基于变压器的语言模型,包括来自变压器的双向编码器表示 (BERT) 和DistilBERT.
- 从PubMed和Reddit数据集中提取了COVID-19的关系.
- 分析了五种不同的语言模型的性能.
主要成果:
- PubMed和Reddit的数据包含了惊人的相似的COVID-19信息.
- 变压器模型成功地从两个来源中提取了实体和关系.
- 语言模型展示了识别新型关系和潜在错误信息的能力.
结论:
- 在健康危机期间,Reddit作为一个有价值的,快速可访问的数据源.
- 变压器语言模型是生物医学知识提取的有效工具.
- 发现未见的关系的能力有助于在流行病期间识别错误信息.
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