SC-CoMIcs: A Superconductivity Corpus for Materials Informatics
SC-CoMIcs: A Superconductivity Corpus for Materials Informatics
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SC-CoMICs：材料信息学超导语料库
DOI：
10.17632/xc9fjz2p3h.2
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Yutaka Sasaki
中科院分区：
文献类型：
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作者：
K. Yamaguchi;R. Asahi;Yutaka Sasaki
本文介绍了一种新的语料库超导材料信息学（MI）的文本挖掘量身定制，命名为超导材料信息学语料库（SC-CoMIcs）。与生物医学信息学不同的是，针对材料科学与工程（MSE）的语料库很少。特别是，没有一个大规模的语料库，可以用来帮助超导材料的搜索。一组材料科学家和自然语言处理专家共同设计了注释，并构建了一个由手动注释的1,000个与超导性相关的MSE摘要组成的语料库。我们用神经命名实体识别（NER）工具在语料库上进行了实验。实验结果表明，在语料库的NER性能是77%左右的微F1，这是人类注释协议率。使用训练好的NER模型，我们自动标注了9,000篇摘要，并创建了一个基于术语相似度的术语检索工具。该工具可以在指定的命名实体类别中找到与查询词相关的超导术语，这表明了我们的SC-CoMIcs的强大功能，有效地从快速扩展的出版物中为材料信息学应用提供知识。
This paper describes a novel corpus tailored for the text mining of superconducting materials in Materials Informatics (MI), named SuperConductivety Corpus for Materials Informatics (SC-CoMIcs). Different from biomedical informatics, there exist very few corpora targeting Materials Science and Engineering (MSE). Especially, there is no sizable corpus which can be used to assist the search of superconducting materials. A team of materials scientists and natural language processing experts jointly designed the annotation and constructed a corpus consisting of manually-annotated 1,000 MSE abstracts related to superconductivity. We conducted experiments on the corpus with a neural Named Entity Recognition (NER) tool. The experimental results show that NER performance over the corpus is around 77% in terms of micro-F1, which is comparable to human annotator agreement rates. Using the trained NER model, we automatically annotated 9,000 abstracts and created a term retrieval tool based on the term similarity. This tool can find superconductivity terms relevant to a query term within a specified Named Entity category, which demonstrates the power of our SC-CoMIcs, efficiently providing knowledge for Materials Informatics applications from rapidly expanding publications.
DOI：
10.1007/bf01303701
发表时间：
1986-01-01
期刊：
ZEITSCHRIFT FUR PHYSIK B-CONDENSED MATTER
影响因子：
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作者：
BEDNORZ, JG;MULLER, KA
通讯作者：
MULLER, KA