Data Augmentation for Historical NER: A Systematic Comparison of Lexical and LLM-based Approaches

2026-05-20 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, quick

Summary

Léa Blinière, Maud Ehrmann, Emanuela Boros, Simon Clematide, and Frederic Kaplan's paper, presented at the 11th Swiss Text Analytics Conference in June 2026, systematically compares data augmentation techniques for Historical Named Entity Recognition (NER). The research focuses on evaluating both lexical and Large Language Model (LLM)-based approaches to enhance datasets for this specialized NLP task. This work aims to provide a comprehensive analysis of how different data augmentation strategies perform in the context of historical texts, where labeled data is often scarce. By contrasting traditional lexical methods with more recent LLM-driven techniques, the authors contribute to understanding effective strategies for improving NER performance on challenging historical linguistic data.

Key takeaway

For NLP Engineers or Research Scientists developing Named Entity Recognition models for historical texts, understanding the efficacy of data augmentation is crucial. This research provides a systematic comparison of lexical and LLM-based approaches, offering insights into which method might best improve your model's performance on scarce historical data. You should consider these findings when selecting data augmentation strategies to enhance NER accuracy in low-resource historical language contexts.

Key insights

This paper systematically compares lexical and LLM-based data augmentation for Historical Named Entity Recognition.

Method

The paper employs a systematic comparison methodology to evaluate lexical and LLM-based data augmentation techniques for Historical Named Entity Recognition.

Topics

• Data Augmentation
• Historical NER
• Large Language Models
• Natural Language Processing
• Lexical Analysis

Best for: AI Scientist, NLP Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.