Unlocking NACE Classification Embeddings with OpenAI for Enhanced Analysis and Processing

TL;DR

This paper introduces a method to convert NACE economic classification data into low-dimensional embeddings that preserve hierarchical relationships, enhancing analysis, visualization, and downstream tasks like clustering and classification.

Contribution

The paper presents a novel approach combining state-of-the-art models and custom metrics to embed NACE classifications while maintaining their hierarchical structure.

Findings

Effective retention of hierarchical information demonstrated

Improved clustering and classification performance

Facilitates visual exploration of economic relationships

Abstract

The Statistical Classification of Economic Activities in the European Community (NACE) is the standard classification system for the categorization of economic and industrial activities within the European Union. This paper proposes a novel approach to transform the NACE classification into low-dimensional embeddings, using state-of-the-art models and dimensionality reduction techniques. The primary challenge is the preservation of the hierarchical structure inherent within the original NACE classification while reducing the number of dimensions. To address this issue, we introduce custom metrics designed to quantify the retention of hierarchical relationships throughout the embedding and reduction processes. The evaluation of these metrics demonstrates the effectiveness of the proposed methodology in retaining the structural information essential for insightful analysis. This approach not only facilitates the visual exploration of economic activity relationships, but also increases the efficacy of downstream tasks, including clustering, classification, integration with other classifications, and others. Through experimental validation, the utility of our proposed framework in preserving hierarchical structures within the NACE classification is showcased, thereby providing a valuable tool for researchers and policymakers to understand and leverage any hierarchical data.