Evidence map›Paper›PMID 34638173›Full record

ArticleGenomics & informatics2021

O-JMeSH: creating a bilingual English-Japanese controlled vocabulary of MeSH UIDs through machine translation and mutual information.

Felipe Soares, Yuka Tateisi, Terue Takatsuki, Atsuko Yamaguchi

Abstract read
In one paragraph

Article in Genomics & informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Felipe SoaresComputer Science Department, The University of Sheffield, Western Bank, Sheffield S10 2TN, UK.
Yuka TateisiNational Bioscience Database Center, Japan Science and Technology Agency, Tokyo 102-8666, Japan.
Terue TakatsukiDatabase Center for Life Science, Research Organization of Information and Systems, Kashiwa 277-0871, Japan.
Atsuko YamaguchiGraduate School of Integrative Science and Engineering, Tokyo City University, Tokyo 158-8557, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Previous approaches to create a controlled vocabulary for Japanese have resorted to existing bilingual dictionary and transformation rules to allow such mappings. However, given the possible new terms introduced due to coronavirus disease 2019 (COVID-19) and the emphasis on respiratory and infection-related terms, coverage might not be guaranteed. We propose creating a Japanese bilingual controlled vocabulary based on MeSH terms assigned to COVID-19 related publications in this work. For such, we resorted to manual curation of several bilingual dictionaries and a computational approach based on machine translation of sentences containing such terms and the ranking of possible translations for the individual terms by mutual information. Our results show that we achieved nearly 99% occurrence coverage in LitCovid, while our computational approach presented average accuracy of 63.33% for all terms, and 84.51% for drugs and chemicals.

Indexed as

controlled vocabularyCOVID-19Japanesemultilingualismnatural language processingtranslation

Identifiers

PMID34638173
PMCPMC8510863

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.