5 papers
Entities, Dates, and Languages: Zero-Shot on Historical Texts with T0
Francesco De Toni, Christopher Akiki, Javier de la Rosa +4
In this work, we explore whether the recently demonstrated zero-shot abilities of the T0 model extend to Named Entity Recognition for out-of-distribution languages and time periods…
Data Centric Domain Adaptation for Historical Text with OCR Errors
Luisa März, Stefan Schweter, Nina Poerner +2
We propose new methods for in-domain and cross-domain Named Entity Recognition (NER) on historical data for Dutch and French. For the cross-domain case, we address domain shift by…
FLERT: Document-Level Features for Named Entity Recognition
Stefan Schweter, Alan Akbik
Current state-of-the-art approaches for named entity recognition (NER) typically consider text at the sentence-level and thus do not model information that crosses sentence boundar…
German's Next Language Model
Branden Chan, Stefan Schweter, Timo Möller
In this work we present the experiments which lead to the creation of our BERT and ELECTRA based German language models, GBERT and GELECTRA. By varying the input training data, mod…
Towards Robust Named Entity Recognition for Historic German
Stefan Schweter, Johannes Baiter
Recent advances in language modeling using deep neural networks have shown that these models learn representations, that vary with the network depth from morphology to semantic rel…