语境词嵌入用于生物医学知识提取:快速审查和案例研究
Dinithi Vithanage1, Ping Yu1, Lei Wang1
1School of Computing and Information Technology, University of Wollongong, Wollongong, NSW 2522 Australia.
Journal of healthcare informatics research
|January 26, 2024
概括
这项研究比较了用于生物医学知识提取的上下文词嵌入模型. 生物BERT在生物医学文本分析方面表现出色,而临床生物BERT最适合临床笔记.
科学领域:
- 自然语言处理 (NLP) 是一种自然语言处理.
- 生物医学信息学 生物医学信息学
- 计算语言学 计算语言学
背景情况:
- 语境词嵌入模型已经从生物医学和医疗保健文本中提取先进的知识.
- 缺乏对这些模型用于生物医学应用的全面比较.
- 现有研究需要对各种生物医学和临床数据的模型性能进行评估.
研究的目的:
- 对生物医学知识提取的上下文词嵌入模型进行范围审查.
- 为了比较主要模型在文本分类,命名实体识别和问题解答方面的性能.
- 确定分析生物医学和临床文档的最有效模型.
主要方法:
- 一项范围审查从26篇文章 (2017-2021) 中确定了18个上下文词嵌入模型.
- 一个案例研究评估了六种代表性模型:ELMo,BERT,BioBERT,BlueBERT,临床BioBERT和GPT-3.
- 通过使用来自推特,NCBI,PubMed和电子健康记录的数据集的准确性和F1分数来评估性能.
主要成果:
- 在分析一般生物医学文本方面,BioBERT表现出卓越的性能.
- 临床BioBERT在分析临床笔记时取得了最佳结果.
- 模型的有效性因特定的NLP任务和数据类型而异.
结论:
- 生物BERT和临床生物BERT分别对生物医学和临床文本分析非常有效.
- 这些发现为在NLP研究中选择合适的词嵌入模型提供了有价值的指导.
- 这项研究有助于有效地从复杂的健康相关文件中提取知识.
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