通过并行边界检测和类别分类来增强生物医学命名实体识别
Yu Wang1, Hanghang Tong2, Ziye Zhu3
1School of Science, China Pharmaceutical University, Nanjing, China. wangyu@cpu.edu.cn.
BMC bioinformatics
|February 25, 2025
概括
我们介绍了BEAN,这是生物医学命名实体识别 (BioNER) 的新型并行模型. BEAN有效地处理嵌套结构和类别相关性,在多个数据集上获得最先进的结果.
科学领域:
- 自然语言处理自然语言处理.
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 生物医学命名实体识别 (BioNER) 对于先进的应用至关重要.
- 由于实体中的嵌套结构和类别相关性,BioNER面临着挑战.
- 现有的模型难以平衡嵌套结构处理和类别知识集成.
研究的目的:
- 为了展示一个新的平行BioNER模型,BEAN.
- 解决生物医学实体的独特特性.
- 为了在BioNER中平衡嵌套结构和类别相关性.
主要方法:
- 开发了一个平行BioNER模型,命名为BEAN.
- 采用了利用头部,尾部和上下文特征进行边界检测的三胺模型.
- 引入了一个多标签分类模型,用于在没有边界指导的情况下提取类别.
主要成果:
- 在五个公共NER数据集 (包括四个生物医学数据集) 上,BEAN实现了最先进的性能.
- 在处理嵌套结构和类别相关性方面证明有效.
- 在实体边界检测和类别分类之间展示了平衡的性能.
结论:
- BEAN是第一个同时处理嵌套结构和类别相关性的BioNER模型.
- 该模型有效地检测实体边界,并对类别进行分类.
- 豆提供了一个有效的方法来BioNER,推进该领域.
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