用Transformer模型提升法语文本多标签关系抽取效果
Extraction multi-étiquettes de relations en utilisant des couches de Transformer
- 融合CamemBERT等预训练模型与Transformer编码器捕捉长距离依赖
- 在TextMine'25数据集上达0.654的宏F1分数，优于FlauBERT-Large
- 适合处理情报报告中复杂关系的自动化提取任务
本文提出BTransformer18模型，一种专为法语文本多标签关系抽取设计的深度学习架构。该方法结合了BERT系列预训练语言模型（如BERT、RoBERTa）及其法语版本CamemBERT和FlauBERT的上下文表征能力，以及Transformer编码器对词元间长程依赖的捕捉优势。在TextMine'25挑战赛数据集上的实验表明，该模型表现优异，尤其使用CamemBERT-Large时，宏F1得分为0.654，超过FlauBERT-Large的结果。这些结果验证了该方法在情报报告中复杂关系自动提取中的有效性。
原文摘要 · Abstract (English)
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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