[4H3-GS-11d] AIと社会：テキスト解析

[4H3-GS-11d-01] A Development of Multi-label Text Classification and Matching System for Achieving SDGs with BERT

〇Kanoko Suzuki¹, Takanori Matsui¹, Shun Kawakubo², Naoki Masuhara³, Asako Iwami⁴, Takashi Machimura¹ (1. Osaka University, 2. Hosei University, 3. Research Institute for Humanity and Nature, 4. The Prefectural University of Kumamoto)

Keywords:Sustainable Development Goals (SDGs), natural language processing, multi-label classification, BERT

Working on SDGs and sharing successful practices with wider stakeholders are important to achieve SDGs. In this study, with a deep-learning natural language processing model, BERT, we aimed to (1) build a classifier that enables to map the meanings of practices and issues to the SDGs context, (2) visualize the nexus between SDGs, and (3) build a matching system between local issues and initiatives which can be solutions. Firstly, documents which were published by the United Nations, and the Japanese Government, and the proposals for solving issues about SDGs that were collected by the Cabinet Office were collected. With those data, a data frame with each document and multi-labels corresponded to SDGs was constructed, and text data augmentation method with WordNet data-base was applied to the data frame. Next, Pretrained Japanese BERT model was fine-tuned by a multi-label text classification task, and nested cross-validation was conducted to optimize the hyperparameters and estimate the cross-validation accuracy. Finally, the co-occurrence network among SDGs was visualized with the fine-tuned BERT model, and a matching system was developed by obtaining cosine similarity between embedded vectors of local issues and initiatives.