# Transformer-Based Named Entity Recognition for French Using Adversarial Adaptation to Similar Domain Corpora

arXiv: https://arxiv.org/abs/2212.03692 · HTML: https://mlsanity.com/paper/2212.03692/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 10 references that are in the map.
- First posted on arXiv 5 Dec 2022.

## Builds on (most cited first, 3 of 3)

- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [CamemBERT: a Tasty French Language Model](https://mlsanity.com/paper/1911.03894/) (2019)
- [FlauBERT: Unsupervised Language Model Pre-training for French](https://mlsanity.com/paper/1912.05372/) (2019)

## Built on by (most cited first, 3 of 3)

- [Radar de Parit'e: An NLP system to measure gender representation in French news stories](https://mlsanity.com/paper/2304.09982/) (2023)
- [Adversarial Adaptation for French Named Entity Recognition](https://mlsanity.com/paper/2301.05220/) (2023)
- [High Accuracy Location Information Extraction from Social Network Texts Using Natural Language Processing](https://mlsanity.com/paper/2308.16615/) (2023)

## Abstract (arXiv)

Named Entity Recognition (NER) involves the identification and classification of named entities in unstructured text into predefined classes. NER in languages with limited resources, like French, is still an open problem due to the lack of large, robust, labelled datasets. In this paper, we propose a transformer-based NER approach for French using adversarial adaptation to similar domain or general corpora for improved feature extraction and better generalization. We evaluate our approach on three labelled datasets and show that our adaptation framework outperforms the corresponding non-adaptive models for various combinations of transformer models, source datasets and target corpora.