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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.15619 |
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| _version_ | 1866917932546654208 |
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| author | Le, Ngoc Luyen Ngompé, Gildas Tagny |
| author_facet | Le, Ngoc Luyen Ngompé, Gildas Tagny |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_15619 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Extraction multi-étiquettes de relations en utilisant des couches de Transformer Le, Ngoc Luyen Ngompé, Gildas Tagny Computation and Language Artificial Intelligence 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. |
| title | Extraction multi-étiquettes de relations en utilisant des couches de Transformer |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2502.15619 |