通过深度学习改进基于字典的命名实体识别
Katerina Nastou1, Mikaela Koutrouli1, Sampo Pyysalo2
1Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3, Copenhagen, 2200, Denmark.
Bioinformatics (Oxford, England)
|September 4, 2024
概括
本研究引入了一种自动化方法,通过生成更好的封锁清单来改进生物医学命名实体识别 (NER). 新方法提高了对基因,疾病,物种和化学物质的文本挖掘精度.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 自然语言处理自然语言处理.
背景情况:
- 基于字典的命名实体识别 (NER) 对于规范化生物医学术语至关重要.
- 目前创建有问题的名称封锁清单的方法是手动的,低效的,并且不能很好地扩展.
- 将NER调整为新的实体类型需要对词典和区块列表进行广泛的手动策划.
研究的目的:
- 开发一种自动化方法,用于生成生物医学NER的改进块清单.
- 通过减少实体识别中的假阳性来提高文本挖掘的精度.
- 为了在不同文档中创建上下文意识的封锁列表,以更准确地识别不同文档的实体.
主要方法:
- 通过比较三个已建立的生物医学NER系统,生成了超过1250万个文本跨度的大数据集.
- 开发了四种实体类型 (基因,疾病,物种,化学物质) 的积极和消极上下文示例.
- 训练了一个基于变压器的模型 (BioBERT) 用于实体类型分类,以识别需要阻止的名称.
主要成果:
- 经过训练的BioBERT模型在实体类型分类中获得了96.7%的高F1得分.
- 自动化方法生成了一个显著更大的封锁列表,翻了一番之前的全文库列表.
- 文本挖掘精度平均增加了约5.5% (化学品超过8.5%,基因超过7.5%),回忆率最小下降0.6%.
结论:
- 自动生成上下文识别封锁清单可大大提高生物医学NER的性能.
- 开发的方法有效地减少了假阳性,提高了像STRING这样的生物数据库的精度.
- 这种方法为维护和改进生物医学NER系统提供了可扩展和高效的解决方案.
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