Abstract
The 17 Sustainable Development Goals (SDGs) are a “shared blueprint for peace and prosperity for people and the planet, now and into the future”. Since 2015, they help pointing out pathways to solve interlinked challenges being faced globally. The monitoring of SDGs is essential to assess progress and obstacles to realise such shared goals. Streams of SDG-related documents produced by governments, academia, private and public entities are assessed by United Nations teams to measure such progress according to each SDG, requiring labelling to proceed to more in-depth analyses. Such laborious task is usually done by the experts, and rely on personal knowledge of the links between the documents contents and the SDGs. While UNEP has experts in many fields, links to the SDGs that are outside their expertise may be overlooked. In this context, we propose to solve this problem with a multi-label classification of texts using Bidirectional Encoder Representations from Transformers (BERT). Based on this method, we designed the SDG-Meter, an online tool able to indicate to the user in a fully automatic way the SDGs linked to their input text but also to quantify the degree of membership of these SDGs.
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Overfitting occurs when the algorithm over-learns (overfit.)in other words, when it learns from data but also from patterns (diagrams, structures) which are not related to the problem, such as noise, thus degrading the performance of the algorithm.
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The original version of BERT is no longer available for the moment because its improved version “SMITH” is under development.
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Guisiano, J.E., Chiky, R., De Mello, J. (2022). SDG-Meter: A Deep Learning Based Tool for Automatic Text Classification of the Sustainable Development Goals. In: Nguyen, N.T., Tran, T.K., Tukayev, U., Hong, TP., Trawiński, B., Szczerbicki, E. (eds) Intelligent Information and Database Systems. ACIIDS 2022. Lecture Notes in Computer Science(), vol 13757. Springer, Cham. https://doi.org/10.1007/978-3-031-21743-2_21
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