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:
- 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)
- PDF:
- https://preview.aclanthology.org/corrections-2024-07/W19-6143.pdf
- Code
- bplank/danish_ner_transfer
- Data
- CoNLL 2003, Universal Dependencies