# TopoLedgerBERT: Topological Learning of Ledger Description Embeddings using Siamese BERT-Networks

arXiv: https://arxiv.org/abs/2407.05175 · HTML: https://mlsanity.com/paper/2407.05175/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 21 references that are in the map.
- First posted on arXiv 19 Apr 2024.

## Builds on (most cited first, 5 of 5)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [BloombergGPT: A Large Language Model for Finance](https://mlsanity.com/paper/2303.17564/) (2023)
- [FinBERT: Financial Sentiment Analysis with Pre-trained Language Models](https://mlsanity.com/paper/1908.10063/) (2019)
- [Extreme Multi-Label Skill Extraction Training using Large Language Models](https://mlsanity.com/paper/2307.10778/) (2023)
- [An Earth Mover's Distance Based Graph Distance Metric For Financial Statements](https://mlsanity.com/paper/2112.07598/) (2021)

## Built on by (most cited first, 2 of 2)

- [A Dutch Financial Large Language Model](https://mlsanity.com/paper/2410.12835/) (2024)
- [Where A Small Language Model Helps in Invoice Categorisation, Understood Through Embedding Geometry](https://mlsanity.com/paper/2608.18033/) (2026)

## Abstract (arXiv)

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.