# Proposal for Automatic Extraction Framework of Superconductors Related Information from Scientific Literature

https://mdr.nims.go.jp/datasets/93dfc968-4dc5-4dd6-bfe7-1131e4fe71d9

## File

- [SC2019-1(nims-1).pdf](https://mdr.nims.go.jp/filesets/f5504c10-ef84-4f83-95b3-644c414a64d0/download) ([Detail](https://mdr.nims.go.jp/filesets/f5504c10-ef84-4f83-95b3-644c414a64d0.md))

## Id

93dfc968-4dc5-4dd6-bfe7-1131e4fe71d9

## Local identifier

identifier: hdl:20.500.11932/test/escidoc:1890245

## Visibility

open_to_public

## State

published

## Created at

2021-08-05T16:21:40.533314Z

## Updated at

2022-10-02T16:50:55.180065Z

## Published at

2021-08-13T18:55:01.958718Z

## Doi



## First published url



## Date published

2019-05-31

## Recorded date published

31/05/2019

## Resource type

journal_article

## Manuscript type

accepted_manuscript

## Collection



## Title

- title: Proposal for Automatic Extraction Framework of Superconductors Related Information
    from Scientific Literature
  title_type: original
  lang: en

## Description

- description: The automatic collection of materials information from research papers
    using Natural Language Processing (NLP) is highly required for rapid materials
    development using big data, namely materials informatics (MI). The difficulty
    of this automatic collection is mainly caused by the variety of expressions in
    the papers, a robust system with tolerance to such variety is required to be developed.
    In this paper, we report an ongoing interdisciplinary work to construct a system
    for automatic collection of superconductor-related information from scientific
    literature using text mining techniques. We focused on the identification of superconducting
    material names and their critical temperature (Tc) key property. We discuss the
    construction of a prototype for extraction and linking using machine learning
    (ML) techniques for the physical information collection. From the evaluation using
    500 sample documents, we define a baseline and a direction for future improvements.
  description_type: abstract
  lang: en

## Creator

- name: FOPPIANO, Luca
  role: author
  orcid: https://orcid.org/0000-0002-6114-6164
- name: Thaer, M. Dieb
  role: author
  orcid: https://orcid.org/0000-0002-8111-2009
- name: SUZUKI, Akira
  role: author
  orcid: https://orcid.org/0000-0002-8167-0414
- name: ISHII, Masashi
  role: author
  orcid: https://orcid.org/0000-0003-0357-2832

## Contact agent



## Publisher



## Managing organization



## Keyword

- subject: TDM
  schema: not_defined
- subject: NLP
  schema: not_defined
- subject: Machine Learning
  schema: not_defined
- subject: Material Informatics
  schema: not_defined
- subject: Superconductors
  schema: not_defined

## Rights



## Other identifier(s)



## Data origin



## Embargo



## Journal



## Conference



## Related item



## Funding



## Instrument



## Instrument operator



## Instrument managing organization



## Measurement method



## Specimen



## Chemical composition



## Structure for specimen



## Structural feature for specimen



## Specific property for specimen



## Process for specimen treatment



## Computational method



## Energy level/transition state



## Software



## Custom property



## Fileset

- id: f5504c10-ef84-4f83-95b3-644c414a64d0
  filename: SC2019-1(nims-1).pdf
  content_type: application/pdf
  size: 774523
  md5: '08c20b02d5f7598e04e78b90f0e23d13'

## Thumbnail

fileset_id: f5504c10-ef84-4f83-95b3-644c414a64d0
filename: SC2019-1(nims-1).pdf