Comparative Analysis of Extrinsic Factors for NER in French

TL;DR

This paper explores various factors including model structure, corpus annotation scheme and data augmentation techniques to improve the performance of a NER model for French and suggests that considering different extrinsic factors and combining these techniques is a promising approach for improving NER performance where the size of data is limited.

Abstract

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.

Figures (4)

• Figure 1: Example of different annotation schemes and data augmentation
• Figure 2: NER results using original corpus
• Figure 3: NER results with POS tags
• Figure 4: NER results with BIOES annotation