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

arXiv: https://arxiv.org/abs/2003.02931 · HTML: https://mlsanity.com/paper/2003.02931/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2020.

## Built on by (most cited first, 5 of 5)

- [Universal NER: A Gold-Standard Multilingual Named Entity Recognition Benchmark](https://mlsanity.com/paper/2311.09122/) (2023)
- [DaN+: Danish Nested Named Entities and Lexical Normalization](https://mlsanity.com/paper/2105.11301/) (2021)
- [HiNER: A Large Hindi Named Entity Recognition Dataset](https://mlsanity.com/paper/2204.13743/) (2022)
- [Different Tastes of Entities: Investigating Human Label Variation in Named Entity Annotations](https://mlsanity.com/paper/2402.01423/) (2024)
- [Cross-lingual German Biomedical Information Extraction: from Zero-shot to Human-in-the-Loop](https://mlsanity.com/paper/2301.09908/) (2023)

## Abstract (arXiv)

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.