欧洲PMC对基因/蛋白质,疾病和生物体进行注释的全文库
Xiao Yang1, Shyamasree Saha1,2, Aravind Venkatesan1
1Literature Services, EMBL-EBI, Wellcome Trust Genome Campus, Cambridge, UK.
Scientific data
|October 19, 2023
概括
一个由人类注释的300篇全文文章的新文献提供了超过72,000个生物医学实体,用于训练机器学习模型在命名实体识别 (NER) 中. 本资源支持用于生物研究的先进自然语言处理 (NLP).
科学领域:
- 生物医学信息学 生物医学信息学
- 自然语言处理自然语言处理.
- 计算生物学 计算生物学
背景情况:
- 命名实体识别 (NER) 是自然语言处理 (NLP) 中的一个关键文本挖掘任务.
- 深度学习模型越来越多地取代了传统的NER方法.
- 现有的生物医学NER数据集往往缺乏全文文章覆盖.
研究的目的:
- 从全文研究文章中开发出高质量的,人类注释的生物医学实体库.
- 为了促进从基于字典到基于机器学习的NER管道的过渡.
- 为推进生物医学NLP研究创建一个开放的社区资源.
主要方法:
- 300篇开放访问的全文研究文章的汇编.
- 人类对基因/蛋白质,疾病和生物体实体的注释.
- 开发一个包含超过114,000个句子的语料库,有超过72,000个注释的提及.
主要成果:
- 创建一个全面的,全文的生物医学NER体.
- 识别了超过72,000个生物医学实体的提及.
- 该库涵盖了基因/蛋白质,疾病和生物.
结论:
- 开发的集体是培训和评估基于深度学习的NER系统的宝贵资源.
- 这个开放的数据集将推动生物医学NLP和文本挖掘领域的发展.
- 该资源可以从科学文献中进行改进的自动信息提取.
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