迈向发现:一个端到端的系统,揭示新的生物医学关系
Tiago Almeida1, Richard A A Jonker1, Rui Antunes1
1IEETA/DETI, LASI, University of Aveiro, Campus Universitário de Santiago, Aveiro 3810-193, Portugal.
概括
这项研究提出了一个新的生物医学关系提取系统,增强了科学文献中的知识发现. 该系统在命名实体识别方面实现了最先进的性能,并在检测新型关系方面取得了竞争性结果.
科学领域:
- 自然语言处理自然语言处理.
- 生物医学信息学 生物医学信息学
- 计算生物学 计算生物学
背景情况:
- 生物医学关系提取对于理解科学文献至关重要,并且在药物发现,精准医学和知识图形构建方面有应用.
- 自动化从研究手稿中提取关系可以显著改善知识发现.
- 生物创意第八届竞赛提出了在生物医学文献中发现新鲜关系的挑战.
研究的目的:
- 开发和评估一个系统,共同提取和分类生物医学实体之间的新型关系.
- 将一个初始系统推进到一个端到端的模型,直接从原始文本中进行全面的关系提取和新性分类.
- 在命名实体识别方面实现最先进的性能,并在新关系检测方面实现竞争性性能.
主要方法:
- 一个最初的系统专注于联合提取和分类新的生物医学关系.
- 该系统被增强为一个级联管道,包含标记器和链接器模块,用于端到端处理.
- 集成系统处理原始文本以提取关系并对它们的新性进行分类.
主要成果:
- 标签模块实现了最先进的命名实体识别性能,微F1得分为90.24.
- 端到端系统在检测新关系方面获得了24.59的竞争性新性F1分数.
- 开发的系统在生物医学关系提取和新奇性检测方面显示出有希望的结果.
结论:
- 开发的端到端系统有效地从生物医学文献中提取和分类新的关系.
- 系统的标签模块在命名实体识别中展示了最先进的性能.
- 公开可用的代码有助于进一步的研究和应用在生物医学知识的发现.
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