用大模型将企业经济活动文本自动归类到国际标准分类，助力循环经济知识库建设。
A Unified Framework to Classify Business Activities into International Standard Industrial Classification through Large Language Models for Circular Economy
- 用微调的GPT-2模型将企业活动描述转为国际标准工业分类（ISIC）
- 在182个标签的数据集上达到95%准确率
- 为全球循环经济推荐系统提供统一分类基础，适合政策与平台开发者
有效的信息收集与知识编码对于推动循环经济实践的推荐系统至关重要。一种有前景的方法是建立一个集中式知识库，记录历史废物资源化交易，从而基于过往成功案例生成推荐。然而，构建此类知识库的主要障碍在于缺乏跨地理区域统一的商业活动表示框架。本文利用大语言模型（LLMs）将描述经济活动的文本数据分类至国际标准产业分类（ISIC），这一全球公认经济活动分类体系。该方法使全球企业提供的任何经济活动描述均可被归入统一的ISIC标准，促进集中式知识库的建立。我们的方法在包含182个标签的测试数据集上实现了95%的准确率，使用微调的GPT-2模型。本研究通过提供标准化的知识编码基础，支持跨区域部署的推荐系统，助力全球可持续循环经济实践。
原文摘要 · Abstract (English)
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 cataloguing 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 endeavour of fostering sustainable circular economy practices by providing a standardized foundation for knowledge codification and recommendation systems deployable across regions.
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