Late fusion of absolute and relative temporal metadata into Transformer NER models produces more robust performance than early fusion on French and German historical datasets, especially in early noisy periods.
In: Proceedings of the Ninth International Workshop on Natural Language Processing for Social Media
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A Study of Temporal Fusion Strategies for Named Entity Recognition in Historical Texts
Late fusion of absolute and relative temporal metadata into Transformer NER models produces more robust performance than early fusion on French and German historical datasets, especially in early noisy periods.