A Unified Framework to Classify Business Activities into International Standard Industrial Classification through Large Language Models for Circular Economy
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A Unified Framework to Classify Business Activities into International Standard Industrial Classification through Large Language Models for Circular Economy
A Unified Framework to Classify Business Activities into International Standard Industrial Classification through Large Language Models for Circular Economy
Journal Title:
2024 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)
Li, X., Zhao, L., Ren, J., Sun, Y., Tan, C. F., Yeo, Z., & Xiao, G. (2024). A Unified Framework to Classify Business Activities into International Standard Industrial Classification through Large Language Models for Circular Economy. 2024 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), 1422–1426. https://doi.org/10.1109/ieem62345.2024.10857123
Abstract:
Effective information gathering and knowledge codification are pivotal for developing recommendation systems that promote circular economy practices. One promising approach involves the creation of a centralized knowledge repository cataloging historical waste-to-resource transactions, which subsequently enables the generation of recommendations based on past successes. However, a significant barrier to constructing such a knowledge repository lies in the absence of a universally standardized framework for representing business activities across disparate geographical regions. To address this challenge,
this paper leverages Large Language Models (LLMs) to classify
textual data describing economic activities into the International
Standard Industrial Classification (ISIC), a globally recognized economic activity classification framework. This approach enables any economic activity descriptions provided by businesses worldwide to be categorized into the unified ISIC standard, facilitating the creation of a centralized knowledge repository.
Our approach achieves a 95% accuracy rate on a 182-label test dataset with fine-tuned GPT-2 model. This research contributes to the global endeavor of fostering sustainable circular economy
practices by providing a standardized foundation for knowledge
codification and recommendation systems deployable across regions
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the Agency for Science, Technology and Research - Central Research Fund - Applied & Translational Research, and Ministry of Education Project “Knowledge infused collaboration platform to foster industrial symbiosis practices
Grant Reference no. : SC26/24-105900