在低资源的生物医学关系提取中利用最短的依赖路径.
Saman Enayati1, Slobodan Vucetic2
1Department of Computer and Information Sciences, Temple University, 1925 N. 12th Street, Suite 304, Philadelphia, PA, 19122, USA. samanenayati@temple.edu.
BMC medical informatics and decision making
|July 25, 2024
概括
最短依赖路径 (SDP) 提高了生物医学关系提取 (RE) 的性能,特别是在有限的标记数据下. 这种方法通过利用句子结构来增强RE分类器,以便更好地识别实体关系.
科学领域:
- 生物医学信息学是生物医学信息学.
- 自然语言处理自然语言处理.
背景情况:
- 生物医学关系提取 (RE) 对于确定生物医学实体之间的关系至关重要.
- 在资源较少的环境中,训练RE模型很困难,标记数据很少.
研究的目的:
- 调查最短依赖路径 (SDPs) 的实用性,以增强生物医学RE.
- 探索在不同学习范式中将SDP集成到单词和句子表示中的方法.
主要方法:
- 使用最短依赖路径 (SDP) 来创建特征表示.
- 应用监督,半监督和上下文学习方法.
- 在三个基准生物医学文本数据集上进行了实验.
主要成果:
- 整合基于SDP的表示方法显著改善了RE分类器的性能.
- 在资源较少的场景中,性能增长特别明显,标记数据最小.
结论:
- SDP 提供了对生物医学文本语法结构的宝贵见解.
- 提出的技术有效地提高了生物医学RE分类器的准确性,特别是在数据稀缺的情况下.
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