SynNER:在生物医学领域的语法注入命名实体识别.
Muhammad Imran1, Olga Zamaraeva1, Carlos Gómez-Rodríguez1
1Universidade da Coruña, CITIC, Departamento de Ciencias de la Computación y Tecnologías de la Información, Campus de Elviña s/n, A Coruña 15071, Spain.
JAMIA open
|February 27, 2026
概括
将显式语法知识集成到神经网络中,可以显著提高生物医学文本处理中的命名实体识别 (NER) 准确性. 这种方法通过利用解析技术来提高关键数据集的性能.
科学领域:
- 计算语言学 计算语言学
- 生物信息学是一种生物信息学.
- 自然语言处理自然语言处理.
背景情况:
- 命名实体识别 (NER) 对于生物医学文本处理至关重要.
- 语法结构有助于识别文本中的实体.
- 目前的NLP方法可以通过结合明确的语言知识来增强.
研究的目的:
- 评估明确语法知识对生物医学NER准确性的影响.
- 通过神经机制研究语法信息的整合.
- 评估NER中依赖性解析和序列标记的好处.
主要方法:
- 利用了通过神经注意力机制集成的明确语法知识.
- 采用了依赖性解析和序列标记解析技术.
- 应用了多任务学习范式,以增强功能表示.
- 在五个不同的生物医学数据集 (MTSamples,VAERS,NCBI-disease,BC2GM,JNLPBA) 上进行了实验.
主要成果:
- 在5个数据集中,在3个数据集中实现了F1比最先进的F1得分的提高.
- 在MTSamples,VAERS和NCBI疾病数据集上表现出增强的性能.
- 减少了特定代币的不匹配,如n-dash和括号.
结论:
- 显式语法特征提高了基于注意力的神经系统中的NER精度.
- 解析作为序列标记为生物医学NER提供了额外的好处.
- 拟议的方法有望改善从生物医学文献中提取信息.
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