View source for Multilingual Named Entity Recognition for Historical Texts

"'''Multilingual Named Entity Recognition for Historical Texts'''" proposes a training and evaluation methodology for NER models that must cope with the spelling variation, code-switching, and non-standard orthography typical of pre-modern European sources. The paper benchmarks several transformer-based architectures against a curated set of historical corpora.

The work underpins the [[Multilingual NER Toolkit]] project, which packages the methodology as reusable models and evaluation harnesses for downstream digital humanities use.