用有限数据提升法语命名实体识别效果
Comparative Analysis of Extrinsic Factors for NER in French
- 对比模型结构、标注方案与数据增强对法语NER的影响
- F1分数从62.41提升至79.39，显著改善性能
- 适合资源受限语言的NER研究者参考
命名实体识别（NER）旨在提取结构化信息，常面临术语复杂、变化多端的挑战。准确可靠的NER有助于重要信息的抽取与分析。然而，除英语外的语言由于数据量有限，标注成本高，实现困难。本文在数据有限条件下，探索模型结构、语料标注方案及数据增强技术对法语NER的影响。实验表明，这些方法可使模型F1分数从原始CRF的62.41大幅提升至79.39。研究结果表明，在数据稀缺场景下，综合考虑多种外部因素并结合相应技术，是提升法语NER性能的有效路径。
原文摘要 · Abstract (English)
Named entity recognition (NER) is a crucial task that aims to identify structured information, which is often replete with complex, technical terms and a high degree of variability. Accurate and reliable NER can facilitate the extraction and analysis of important information. However, NER for other than English is challenging due to limited data availability, as the high expertise, time, and expenses are required to annotate its data. In this paper, by using the limited data, we explore various factors including model structure, corpus annotation scheme and data augmentation techniques to improve the performance of a NER model for French. Our experiments demonstrate that these approaches can significantly improve the model's F1 score from original CRF score of 62.41 to 79.39. Our findings suggest that considering different extrinsic factors and combining these techniques is a promising approach for improving NER performance where the size of data is limited.
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