Neural Cross-Lingual Transfer and Limited Annotated Data for Named Entity Recognition in Danish

Barbara Plank


Abstract
Named Entity Recognition (NER) has greatly advanced by the introduction of deep neural architectures. However, the success of these methods depends on large amounts of training data. The scarcity of publicly-available human-labeled datasets has resulted in limited evaluation of existing NER systems, as is the case for Danish. This paper studies the effectiveness of cross-lingual transfer for Danish, evaluates its complementarity to limited gold data, and sheds light on performance of Danish NER.
Anthology ID:
W19-6143
Volume:
Proceedings of the 22nd Nordic Conference on Computational Linguistics
Month:
September–October
Year:
2019
Address:
Turku, Finland
Editors:
Mareike Hartmann, Barbara Plank
Venue:
NoDaLiDa
SIG:
Publisher:
Linköping University Electronic Press
Note:
Pages:
370–375
Language:
URL:
https://aclanthology.org/W19-6143
DOI:
Bibkey:
Cite (ACL):
Barbara Plank. 2019. Neural Cross-Lingual Transfer and Limited Annotated Data for Named Entity Recognition in Danish. In Proceedings of the 22nd Nordic Conference on Computational Linguistics, pages 370–375, Turku, Finland. Linköping University Electronic Press.
Cite (Informal):
Neural Cross-Lingual Transfer and Limited Annotated Data for Named Entity Recognition in Danish (Plank, NoDaLiDa 2019)
Copy Citation:
PDF:
https://aclanthology.org/W19-6143.pdf
Code
 bplank/danish_ner_transfer
Data
CoNLL 2003Universal Dependencies