无监督的词嵌入从材料科学文学中捕获潜在的知识
Vahe Tshitoyan1,2, John Dagdelen3,4, Leigh Weston3
1Lawrence Berkeley National Laboratory, Berkeley, CA, USA. vahe.tshitoyan@gmail.com.
Nature
|July 5, 2019
概括
本研究引入了无监督的词嵌入来从文本中提取材料科学知识. 这种方法捕捉复杂的概念,并从已发表的文献中预测未来的发现.
科学领域:
- 材料科学 材料科学 材料科学
- 自然语言处理自然语言处理.
- 计算化学计算化学
背景情况:
- 科学知识主要以文本形式存在,这给传统和机器学习方法带来了分析挑战.
- 结构化财产数据库提供机器可解释的数据,但涵盖了有限部分的科学文献.
- 监督自然语言处理 (NLP) 用于信息检索需要大量的手工标记数据集.
研究的目的:
- 从科学文献中开发一种无监督的方法来编码材料科学知识.
- 为了证明这种方法在没有人类监督的情况下捕捉复杂的科学概念的能力.
- 从现有出版物中展示预测未来材料发现的潜力.
主要方法:
- 利用信息密集的词嵌入 (词的矢量表示) 来编码科学文本.
- 应用了无监督的机器学习方法,消除了对人类标签的需求.
- 训练有素的嵌入在大量的科学文献中.
主要成果:
- 词嵌入成功捕获隐含的材料科学知识,包括周期表结构和结构-属性关系.
- 无监督方法在实际发现之前的几年内预测了应用程序的功能材料.
- 证明了未来发现的潜在知识嵌入了过去的出版物.
结论:
- 无监督的词嵌入提供了一种有效的方式来提取和利用科学文献中的知识.
- 这种方法使集体知识提取成为可能,并提出了科学文献挖掘的通用方法.
- 这些发现为发现新材料和通过自动知识提取加速科学研究开辟了道路.
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