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.
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.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 22nd Nordic Conference on Computational Linguistics (NoDaLiDa’19) . |
| Publisher | Association for Computational Linguistics |
| Publication date | 2019 |
| ISBN (Electronic) | 978-91-7929-995-8 |
| Publication status | Published - 2019 |
| Series | NEALT (Northern European Association of Language Technology) Proceedings Series |
|---|---|
| ISSN | 1736-6305 |
Keywords
- Named Entity Recognition
- Deep Neural Architectures
- Cross-Lingual Transfer
- Training Data Scarcity
- Danish Language Processing