NaDev: An Annotated Corpus to Support Information Extraction from Research Papers on Nanocrystal Devices

Dieb, Thaer M.; Yoshioka, Masaharu; Hara, Shinjiro

doi:10.2197/ipsjjip.24.554

Cited by 1 publication

(2 citation statements)

References 30 publications

(48 reference statements)

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“…The annotation workflow (Figure 2) was designed following the MATTER (Model, Annotate, Train, Test, Evaluate, and Revise) schema [41] and other related work [11,15]. The workflow is composed of five steps (Figure 2): data-preparation, correction, validation, testing and evaluation, revision.…”

Section: Annotation Processmentioning

confidence: 99%

“…In comparison, such resources in the materials science domain are rather limited. Reported cases include NaDev [15] on nanocrystal devices research, a corpus for extracting synthesis recipes [16], and ChemDataExtractor [17] which focuses only on chemical entities. In the superconductors domain, we could identify MagDb [18] focusing on magnetic materials with limited information categories.…”

Section: Introductionmentioning

confidence: 99%

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SuperMat: construction of a linked annotated dataset from superconductors-related publications

Foppiano¹,

Dieb²,

Suzuki³

et al. 2021

Science and Technology of Advanced Materials: Methods

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A growing number of papers are published in the area of superconducting materials science. However, novel text and data mining (TDM) processes are still needed to efficiently access and exploit this accumulated knowledge, paving the way towards data-driven materials design. Herein, we present SuperMat (Superconductor Materials), an annotated corpus of linked data derived from scientific publications on superconductors, which comprises 142 articles, 16052 entities, and 1398 links that are characterised into six categories: the names, classes, and properties of materials; links to their respective superconducting critical temperature (Tc); and parametric conditions such as applied pressure or measurement methods. The construction of SuperMat resulted from a fruitful collaboration between computer scientists and material scientists, and its high quality is ensured through validation by domain experts. The quality of the annotation guidelines was ensured by satisfactory Inter Annotator Agreement (IAA) between the annotators and the domain experts. SuperMat includes the dataset, annotation guidelines, and annotation support tools that use automatic suggestions to help minimise human errors.

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Section: Annotation Processmentioning

confidence: 99%