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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.

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概括
此摘要是机器生成的。

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.

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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.