Extraction multi-\'etiquettes de relations en utilisant des couches de Transformer

Ngoc Luyen Le; Gildas Tagny Ngompé

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arXiv · 2502.15619

Extraction multi-\'etiquettes de relations en utilisant des couches de Transformer

Ngoc Luyen Le·Gildas Tagny Ngompé

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Abstract

In this article, we present the BTransformer18 model, a deep learning architecture designed for multi-label relation extraction in French texts. Our approach combines the contextual representation capabilities of pre-trained language models from the BERT family - such as BERT, RoBERTa, and their French counterparts CamemBERT and FlauBERT - with the power of Transformer encoders to capture long-term dependencies between tokens. Experiments conducted on the dataset from the TextMine'25 challenge show that our model achieves superior performance, particularly when using CamemBERT-Large, with a macro F1 score of 0.654, surpassing the results obtained with FlauBERT-Large. These results demonstrate the effectiveness of our approach for the automatic extraction of complex relations in intelligence reports.

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Ngoc Luyen Le, Gildas Tagny Ngompé. 2025-02-21. Extraction multi-\'etiquettes de relations en utilisant des couches de Transformer. https://arxiv.org/abs/2502.15619

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