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Treffer: A natural language processing model for supporting sustainable development goals: translating semantics, visualizing nexus, and connecting stakeholders.

Title:
A natural language processing model for supporting sustainable development goals: translating semantics, visualizing nexus, and connecting stakeholders.
Authors:
Matsui T; Division of Sustainable Energy and Environmental Engineering, Graduate School of Engineering, Osaka University, Yamadaoka 2-1, Suita, Osaka 565-0871 Japan., Suzuki K; Division of Sustainable Energy and Environmental Engineering, Graduate School of Engineering, Osaka University, Yamadaoka 2-1, Suita, Osaka 565-0871 Japan., Ando K; Division of Sustainable Energy and Environmental Engineering, Graduate School of Engineering, Osaka University, Yamadaoka 2-1, Suita, Osaka 565-0871 Japan., Kitai Y; Department of Architecture, Faculty of Engineering and Design, Hosei University, 2-33 Ichigayatamachi, Shinjuku, Tokyo 162-0843 Japan., Haga C; Division of Sustainable Energy and Environmental Engineering, Graduate School of Engineering, Osaka University, Yamadaoka 2-1, Suita, Osaka 565-0871 Japan., Masuhara N; School of Human Science and Environment, University of Hyogo, Shinzaike-honcho 1-1-12, Himeji, Hyogo 670-0092 Japan., Kawakubo S; Department of Architecture, Faculty of Engineering and Design, Hosei University, 2-33 Ichigayatamachi, Shinjuku, Tokyo 162-0843 Japan.
Source:
Sustainability science [Sustain Sci] 2022; Vol. 17 (3), pp. 969-985. Date of Electronic Publication: 2022 Feb 04.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Springer Country of Publication: Japan NLM ID: 101731366 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1862-4057 (Electronic) Linking ISSN: 18624057 NLM ISO Abbreviation: Sustain Sci Subsets: PubMed not MEDLINE
Imprint Name(s):
Original Publication: Japan : Springer, ©2006-
References:
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Sustain Sci. 2020;15(6):1561-1572. (PMID: 33133296)
Glob Chall. 2017 Nov 13;1(9):1700036. (PMID: 31565295)
BMC Bioinformatics. 2006 Feb 23;7:91. (PMID: 16504092)
Sci Total Environ. 2020 Mar 25;710:136014. (PMID: 32050357)
Nat Commun. 2020 Jan 13;11(1):233. (PMID: 31932590)
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Sci Total Environ. 2018 Sep 1;635:1191-1204. (PMID: 29710574)
J Environ Manage. 2019 May 15;238:126-135. (PMID: 30849597)
Contributed Indexing:
Keywords: Artificial intelligence technology; BERT model; Matchmaking stakeholders; Nexus and interlinkages; Sustainable development goals; Text classification
Entry Date(s):
Date Created: 20220209 Latest Revision: 20220716
Update Code:
20250114
PubMed Central ID:
PMC8815292
DOI:
10.1007/s11625-022-01093-3
PMID:
35136451
Database:
MEDLINE

Weitere Informationen

Sharing successful practices with other stakeholders is important for achieving SDGs. In this study, with a deep-learning natural language processing model, bidirectional encoder representations from transformers (BERT), the authors aimed to build (1) a classifier that enables semantic mapping of practices and issues in the SDGs context, (2) a visualizing method of SDGs nexus based on co-occurrence of goals (3) a matchmaking process between local issues and initiatives that may embody solutions. A data frame was built using documents published by official organizations and multi-labels corresponding to SDGs. A pretrained Japanese BERT model was fine-tuned on a multi-label text classification task, while nested cross-validation was conducted to optimize the hyperparameters and estimate cross-validation accuracy. A system was then developed to visualize the co-occurrence of SDGs and to couple the stakeholders by evaluating embedded vectors of local challenges and solutions. The paper concludes with a discussion of four future perspectives to improve the natural language processing system. This intelligent information system is expected to help stakeholders take action to achieve the sustainable development goals.
Supplementary Information: The online version contains supplementary material available at 10.1007/s11625-022-01093-3.
(© The Author(s) 2022.)