13 citations · 14 across the 4 of their papers we have counts for
5 papers
hmBERT: Historical Multilingual Language Models for Named Entity Recognition
Stefan Schweter, Luisa März, Katharina Schmid +1
Compared to standard Named Entity Recognition (NER), identifying persons, locations, and organizations in historical texts constitutes a big challenge. To obtain machine-readable c…
XPASC: Measuring Generalization in Weak Supervision by Explainability and Association
Luisa März, Ehsaneddin Asgari, Fabienne Braune +2
Weak supervision is leveraged in a wide range of domains and tasks due to its ability to create massive amounts of labeled data, requiring only little manual effort. Standard appro…
KnowMAN: Weakly Supervised Multinomial Adversarial Networks
Luisa März, Ehsaneddin Asgari, Fabienne Braune +2
The absence of labeled data for training neural models is often addressed by leveraging knowledge about the specific task, resulting in heuristic but noisy labels. The knowledge is…
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…
Domain adaptation for part-of-speech tagging of noisy user-generated text
Luisa März, Dietrich Trautmann, Benjamin Roth
The performance of a Part-of-speech (POS) tagger is highly dependent on the domain ofthe processed text, and for many domains there is no or only very little training data availabl…