基于西班牙临床编码器模型对NER和分类任务的比较分析
Guillem García Subies1,2, Álvaro Barbero Jiménez2, Paloma Martínez Fernández1
1Computer Science Department, Universidad Carlos III de Madrid, Leganés, Spain.
概括
在西班牙临床任务中,通用编码器模型的性能优于专门的临床模型. 最好的模型,RigoBERTa 2,获得了0.880 F1分,突出了对更多样化的临床体和先进的西班牙NLP模型的需求.
科学领域:
- 自然语言处理 (NLP) 是一种自然语言处理.
- 计算语言学 计算语言学
- 医疗信息学 医疗信息学
背景情况:
- 在西班牙语的NLP资源中存在很大的差距,特别是在临床领域.
- 有效的西班牙语模型对于临床研究和医疗保健提供至关重要,因为西班牙语人口众多,电子健康记录的使用越来越多.
研究的目的:
- 评估编码器语言模型在西班牙语临床任务的有效性.
- 为这些任务确定最有效的西班牙语和临床语言模型.
主要方法:
- 评估了17个不同的体,重点是临床任务.
- 西班牙语模型和西班牙临床语言模型的基准测试 (基于编码器).
- 微调超过3000个模型.
主要成果:
- 一般用途的编码器模型的表现优于专门的临床模型.
- 较大的模型并不总是优越;通用模型RigoBERTa 2获得了最高的平均F1分数0.880.
- 该研究确定RigoBERTa 2是表现最好的模型.
结论:
- 基于专门的编码器的西班牙临床语言模型比生成模型具有优势.
- 多样化的公司稀缺,主要集中在命名实体识别 (NER) 上,需要进一步的研究.
- 高性能模型的有限可用性凸显了西班牙临床NLP持续发展的迫切需要.
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