Unlocking NACE Classification Embeddings with OpenAI for Enhanced Analysis and Processing

ArXiv ID: 2409.11524 “View on arXiv”

Authors: Unknown

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.

Keywords: Dimensionality Reduction, Hierarchical Classification, Embeddings, NACE, Data Visualization, General/Infrastructure

Complexity vs Empirical Score

• Math Complexity: 6.0/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced dimensionality reduction techniques (PCA, t-SNE, UMAP) and custom metrics for hierarchical preservation, indicating moderate mathematical complexity. It is highly empirical, featuring experimental validation with specific datasets, implementation details, and metrics suitable for backtesting and practical application.

  flowchart TD
    A["Research Goal: Create NACE embeddings<br>Preserving Hierarchical Structure"] --> B["Methodology: Custom Metric Integration"]
    B --> C["Input: NACE Classification Data"]
    C --> D["Processing: OpenAI Embeddings &<br>Dimensionality Reduction"]
    D --> E["Validation: Hierarchical Retention Metrics"]
    E --> F["Outcome: Visual Exploration Tools"]
    E --> G["Outcome: Enhanced Downstream Tasks"]
    E --> H["Outcome: Universal Hierarchical Framework"]