TopoLedgerBERT: Topological Learning of Ledger Description Embeddings using Siamese BERT-Networks
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866929411862822912 |
|---|---|
| author | Noels, Sander Viaene, Sébastien De Bie, Tijl |
| author_facet | Noels, Sander Viaene, Sébastien De Bie, Tijl |
| contents | This paper addresses a long-standing problem in the field of accounting: mapping company-specific ledger accounts to a standardized chart of accounts. We propose a novel solution, TopoLedgerBERT, a unique sentence embedding method devised specifically for ledger account mapping. This model integrates hierarchical information from the charts of accounts into the sentence embedding process, aiming to accurately capture both the semantic similarity and the hierarchical structure of the ledger accounts. In addition, we introduce a data augmentation strategy that enriches the training data and, as a result, increases the performance of our proposed model. Compared to benchmark methods, TopoLedgerBERT demonstrates superior performance in terms of accuracy and mean reciprocal rank. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_05175 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | TopoLedgerBERT: Topological Learning of Ledger Description Embeddings using Siamese BERT-Networks Noels, Sander Viaene, Sébastien De Bie, Tijl Computational Engineering, Finance, and Science Computation and Language This paper addresses a long-standing problem in the field of accounting: mapping company-specific ledger accounts to a standardized chart of accounts. We propose a novel solution, TopoLedgerBERT, a unique sentence embedding method devised specifically for ledger account mapping. This model integrates hierarchical information from the charts of accounts into the sentence embedding process, aiming to accurately capture both the semantic similarity and the hierarchical structure of the ledger accounts. In addition, we introduce a data augmentation strategy that enriches the training data and, as a result, increases the performance of our proposed model. Compared to benchmark methods, TopoLedgerBERT demonstrates superior performance in terms of accuracy and mean reciprocal rank. |
| title | TopoLedgerBERT: Topological Learning of Ledger Description Embeddings using Siamese BERT-Networks |
| topic | Computational Engineering, Finance, and Science Computation and Language |
| url | https://arxiv.org/abs/2407.05175 |