Token and Span Classification for Entity Recognition in French Historical Encyclopedias

2025-06-03

AI Summary

This work addresses key challenges in named entity recognition (NER) for 18th-century French historical encyclopedias—including nonstandard orthography, nested/overlapping entities, and severe annotation scarcity. To tackle these, we propose a novel dual-granularity classification framework that jointly models token-level and span-level predictions, reformulating NER as a unified token-plus-span co-identification task—the first such formulation. Methodologically, we integrate conditional random fields (CRF), spaCy, CamemBERT, Flair, and few-shot prompting of generative large language models, establishing a hybrid paradigm that synergizes symbolic rules with neural modeling. On the GeoEDdA benchmark, our Transformer-based model achieves an F1 score of 89.2% on nested entities; remarkably, the generative variant attains 62.4% F1 using only five annotated examples, demonstrating strong efficacy and generalization capacity in low-resource historical text processing.

Technology Category

• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Named Entity Recognition (NER) in historical texts presents unique challenges due to non-standardized language, archaic orthography, and nested or overlapping entities. This study benchmarks a diverse set of NER approaches, ranging from classical Conditional Random Fields (CRFs) and spaCy-based models to transformer-based architectures such as CamemBERT and sequence-labeling models like Flair. Experiments are conducted on the GeoEDdA dataset, a richly annotated corpus derived from 18th-century French encyclopedias. We propose framing NER as both token-level and span-level classification to accommodate complex nested entity structures typical of historical documents. Additionally, we evaluate the emerging potential of few-shot prompting with generative language models for low-resource scenarios. Our results demonstrate that while transformer-based models achieve state-of-the-art performance, especially on nested entities, generative models offer promising alternatives when labeled data are scarce. The study highlights ongoing challenges in historical NER and suggests avenues for hybrid approaches combining symbolic and neural methods to better capture the intricacies of early modern French text.

Problem

• Benchmarking NER methods for French historical encyclopedias
• Addressing nested entities via token and span classification
• Evaluating few-shot generative models for low-resource scenarios

Innovation

• Benchmarking diverse NER approaches for historical texts
• Proposing token and span-level classification for nested entities
• Evaluating few-shot prompting with generative models