通过词嵌入识别知识的新元素
Deyun Yin1,2, Zhao Wu1, Kazuki Yokota3
1School of Economics and Management, Harbin Institute of Technology (Shenzhen), Shenzhen, China.
PloS one
|June 20, 2023
概括
本研究引入了一种使用机器学习在科学文件中测量元素新性的新方法. 这种方法得到了验证,并且具有普遍的领域,与科学家的自我报告发现有很好的相关性.
科学领域:
- 科学发现和创新是科学发现和创新.
- 圣经计量和科学计量.
- 计算语言学计算语言学
背景情况:
- 新性在科学中至关重要,但现有的措施有局限性.
- 以前的方法往往侧重于重组新性,忽视元素新性.
- 许多新措施缺乏验证和现场普遍性.
研究的目的:
- 开发一个验证的,领域通用的方法来计算元素的新性.
- 解决以前科学新性评估方法的局限性.
- 在科学文件中量化个别知识元素的新性.
主要方法:
- 利用机器学习开发一个词嵌入模型用于语义信息提取.
- 通过测量文档在语义空间中的距离来量化元素的新性.
- 通过对800名科学家进行问卷调查来验证该方法.
主要成果:
- 词嵌入模型有效地捕获语义信息.
- 拟议的元素新性测量与科学家自我报告的新性有显著的相关性.
- 这种相关性适用于各种科学领域和发现类型 (例如现象,物质).
结论:
- 开发元素的新性措施是一个可靠和广泛适用的工具.
- 这种方法提供了一种验证的方法来评估科学原创性.
- 这些发现支持使用计算方法来评估科学影响.
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