# Transformer-based Entity Legal Form Classification

arXiv: https://arxiv.org/abs/2310.12766 · HTML: https://mlsanity.com/paper/2310.12766/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 27 references that are in the map.
- First posted on arXiv 19 Oct 2023.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [FinBERT: A Pretrained Language Model for Financial Communications](https://mlsanity.com/paper/2006.08097/) (2020)
- [Deep Learning Based Text Classification: A Comprehensive Review](https://mlsanity.com/paper/2004.03705/) (2020)
- [Multilingual is not enough: BERT for Finnish](https://mlsanity.com/paper/1912.07076/) (2019)
- [BERTje: A Dutch BERT Model](https://mlsanity.com/paper/1912.09582/) (2019)
- [Spanish Pre-trained BERT Model and Evaluation Data](https://mlsanity.com/paper/2308.02976/) (2023)

## Built on by (most cited first, 1 of 1)

- [Corporate-Family Resolution Is Not a String-Matching Problem: A Public Benchmark Stratified by Name Visibility](https://mlsanity.com/paper/2609.04269/) (2026)

## Abstract (arXiv)

We propose the application of Transformer-based language models for classifying entity legal forms from raw legal entity names. Specifically, we employ various BERT variants and compare their performance against multiple traditional baselines. Our evaluation encompasses a substantial subset of freely available Legal Entity Identifier (LEI) data, comprising over 1.1 million legal entities from 30 different legal jurisdictions. The ground truth labels for classification per jurisdiction are taken from the Entity Legal Form (ELF) code standard (ISO 20275). Our findings demonstrate that pre-trained BERT variants outperform traditional text classification approaches in terms of F1 score, while also performing comparably well in the Macro F1 Score. Moreover, the validity of our proposal is supported by the outcome of third-party expert reviews conducted in ten selected jurisdictions. This study highlights the significant potential of Transformer-based models in advancing data standardization and data integration. The presented approaches can greatly benefit financial institutions, corporations, governments and other organizations in assessing business relationships, understanding risk exposure, and promoting effective governance.