OSPAR:一个用参数角色提取有机合成程序的集体
Kojiro Machi1, Seiji Akiyama2, Yuuya Nagata2
1Graduate School of Information Science and Technology, Hokkaido University, Kita 14, Nishi 9, Kita-ku, Sapporo, Hokkaido 060-0814, Japan.
Journal of chemical information and modeling
|October 20, 2023
概括
本研究介绍了用于自动化化学反应提取的有机合成过程与参数角色 (OSPAR) 语料库. OSPAR提高了语义理解,使科学文档中的信息检索更准确.
科学领域:
- 化学 化学 化学
- 自然语言处理自然语言处理.
- 计算语言学 计算语言学
背景情况:
- 由于越来越多的科学文献,自动提取化学反应信息至关重要.
- 现有的程序提取方法往往缺乏语义理解,或者过于专注于严格的方案.
- 存在对注释体的需求,以捕捉化学过程中的语义关系.
研究的目的:
- 开发一个新的注释体,有机合成程序与参数角色 (OSPAR),用于化学反应信息提取.
- 在有机合成中建立定义行动和论点之间的语义关系的角色集.
- 促进化学文献的先进自然语言处理工具的开发.
主要方法:
- 通过注释"有机合成"中的112种有机合成程序,创建OSPAR库.
- 定义了19个新的角色集,并整合了来自Proposition Bank的29个角色集.
- 在OSPAR集体上训练的深度学习系统的开发,用于程序提取.
主要成果:
- 在OSPAR集体中,为化学反应提供了详细的语义角色注释.
- 在OSPAR上训练的深度学习系统展示了与SynthReader等现有工具相比较的信息覆盖范围.
- 系统的输出与原始文本的对齐使得行动序列的简单验证变得容易.
结论:
- OSPAR集体是推动自动化化学反应信息提取的宝贵资源.
- 提出的角色组有效地捕捉了对于理解化学过程至关重要的语义关系.
- 这项工作有助于改进化学中的NLP应用,使得科学文本中的数据挖掘更好.
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