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OSDG -- Open-Source Approach to Classify Text Data by UN Sustainable Development Goals (SDGs)

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arxiv 2005.14569 v1 pith:U4VGIM5V submitted 2020-05-29 cs.DL

Lukas Pukelis , Nuria Bautista Puig , Mykola Skrynik , Vilius Stanciauskas This is my paper · ORCID
classification cs.DL
keywords developmentgoalsresearchsdgsfeaturesosdgprevioussustainable
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Sustainable Development Goals (SDGs) bring together the diverse development community and provide a clear set of development targets for 2030. Given a large number of actors and initiatives related to these goals, there is a need to have a way to accurately and reliably assign text to different input: scientific research, research projects, technological output or documents to specific SDGs. In this paper we present Open Source SDG (OSDG) project and tool which does so by integrating existing research and previous classification into a robust and coherent framework. This integration is based on linking the features from the variety of previous approaches, like ontology items, keywords or features from machine-learning models, to the topics in Microsoft Academic Graph.

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  1. A Comparative Study of Task Adaptation Techniques of Large Language Models for Identifying Sustainable Development Goals

    cs.CL 2025-06 conditional novelty 5.0 of 10

    On a balanced benchmark built from the OSDG community dataset, fine-tuned LLaMa-2 13B achieves the highest macro F1 (92.4%), and small models such as Flan-T5-base (220M) reach 90.5%, close to fine-tuned GPT-3.5 (91.4%).

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