arXiv:2606.27881v1 Announce Type: cross Abstract: Temporal variation poses a unique challenge for named entity recognition (NER) in historical texts, where entities drift in surface form and salience across time. While language models (LMs) have made progress in various NLP tasks, their ability to reason about temporality, especially in diachronic contexts, remains limited or at least, questionable. In this paper, we systematically study how temporal metadata can be structurally embedded into NER models using a range of lightweight fusion strategies. We experiment with both absolute and relati
The increasing sophistication of language models presents a timely opportunity to address nuanced challenges like temporal variation in historical named entity recognition, pushing the boundaries of AI capabilities.
Improving NER in historical texts is crucial for digital humanities, historical research, and cultural heritage, enabling more accurate and efficient analysis of vast archives.
The ability to integrate temporal metadata systematically into NER models means historical documents can be processed with greater accuracy regarding evolving entities and their significance over time.
- · Digital Humanities
- · Historians
- · Cultural Heritage Institutions
- · Natural Language Processing Researchers
- · Manual historical text analysis
- · Generic NER models
More accurate digital indexing and search capabilities for historical archives will emerge.
New insights derived from historical datasets previously too complex for automated analysis will become possible.
This could lead to a deeper, data-driven understanding of diachronic cultural and linguistic evolution, potentially informing future AI development for dynamic environments.
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Read at arXiv cs.AI