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

ABSTRACT: 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 cros

SUBMITTER: Matsui T 

PROVIDER: S-EPMC8815292 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature