Salvato in:
| Autori principali: | , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2409.11524 |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866913505574125568 |
|---|---|
| author | Vidali, Andrea Jean, Nicola Pera, Giacomo Le |
| author_facet | Vidali, Andrea Jean, Nicola Pera, Giacomo Le |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_11524 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Unlocking NACE Classification Embeddings with OpenAI for Enhanced Analysis and Processing Vidali, Andrea Jean, Nicola Pera, Giacomo Le Machine Learning General Economics Economics Statistical Finance 62M45, 91G40 G.3; I.2 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. |
| title | Unlocking NACE Classification Embeddings with OpenAI for Enhanced Analysis and Processing |
| topic | Machine Learning General Economics Economics Statistical Finance 62M45, 91G40 G.3; I.2 |
| url | https://arxiv.org/abs/2409.11524 |