# Automatic Identification and Normalisation of Physical Measurements in Scientific Literature 

https://mdr.nims.go.jp/datasets/c861221b-e03e-4fc9-8cf6-8a4165174f34

## File

- [main.pdf](https://mdr.nims.go.jp/filesets/010da261-ffe7-49e9-ad13-2e630457f85f/download) ([Detail](https://mdr.nims.go.jp/filesets/010da261-ffe7-49e9-ad13-2e630457f85f.md))

## Id

c861221b-e03e-4fc9-8cf6-8a4165174f34

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## Visibility

open_to_public

## State

published

## Created at

2021-08-05T16:24:13.465874Z

## Updated at

2022-10-02T17:00:14.378951Z

## Published at

2021-08-12T16:20:03.972857Z

## Doi

https://doi.org/10.48505/nims.3039

## First published url

https://doi.org/10.1145/3342558.3345411

## Date published

2019-09-23

## Recorded date published

2019-9-23

## Resource type

journal_article

## Manuscript type

authors_original

## Collection



## Title

- title: 'Automatic Identification and Normalisation of Physical Measurements in Scientific
    Literature '
  title_type: original
  lang: en

## Description

- description: 'We present Grobid-quantities, an open-source application for extracting
    and normalising measurements from scientific and patent literature. Tools of this
    kind, aiming to understand and make unstructured information accessible, represent
    the building blocks for large-scale Text and Data Mining (TDM) systems. Grobid-quantities
    is a module built on top of Grobid [6] [13], a machine learning framework for
    parsing and structuring PDF documents. Designed to process large quantities of
    data, it provides a robust implementation accessible in batch mode or via a REST
    API. The machine learning engine architecture follows the cascade approach, where
    each model is specialised in the resolution of a specific task. The models are
    trained using CRF (Conditional Random Field) algorithm [12] for extracting quantities
    (atomic values, intervals and lists), units (such as length, weight) and different
    value representations (numeric, alphabetic or scientific notation). Identified
    measurements are normalised according to the International System of Units (SI).
    Thanks to its stable recall and reliable precision, Grobid-quantities has been
    integrated as the measurement-extraction engine in various TDM projects, such
    as Marve (Measurement Context Extraction from Text), for extracting semantic measurements
    and meaning in Earth Science [10]. At the National Institute for Materials Science
    in Japan (NIMS), it is used in an ongoing project to discover new superconducting
    materials. Normalised materials characteristics (such as critical temperature,
    pressure) extracted from scientific literature are a key resource for materials
    informatics (MI) [9]. '
  description_type: abstract
  lang: en

## Creator

- name: FOPPIANO, Luca
  role: author
  orcid: https://orcid.org/0000-0002-6114-6164
- name: ROMARY, Laurent
  role: author
  orcid: https://orcid.org/0000-0002-0756-0508
- name: ISHII, Masashi
  role: author
  orcid: https://orcid.org/0000-0003-0357-2832
- name: TANIFUJI, Mikiko
  role: author
  orcid: https://orcid.org/0000-0001-5284-6364

## Contact agent



## Publisher

organization: Association for Computing Machinery

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## Keyword

- subject: tdm
  schema: not_defined
- subject: physical quantities
  schema: not_defined
- subject: machine learning
  schema: not_defined

## Rights

- description: In Copyright
  identifier: http://rightsstatements.org/vocab/InC/1.0/

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## Fileset

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  filename: main.pdf
  content_type: application/pdf
  size: 518394
  md5: 8897cee65d8f48a67499c1cf990ff2da

## Thumbnail

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filename: main.pdf