# DaN+: Danish Nested Named Entities and Lexical Normalization

arXiv: https://arxiv.org/abs/2105.11301 · HTML: https://mlsanity.com/paper/2105.11301/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 48 references that are in the map.
- First posted on arXiv 24 May 2021.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [Word Translation Without Parallel Data](https://mlsanity.com/paper/1710.04087/) (2017)
- [Neural Unsupervised Domain Adaptation in NLP---A Survey](https://mlsanity.com/paper/2006.00632/) (2020)
- [A General Framework for Information Extraction using Dynamic Span Graphs](https://mlsanity.com/paper/1904.03296/) (2019)
- [Massive Choice, Ample Tasks (MaChAmp): A Toolkit for Multi-task Learning in NLP](https://mlsanity.com/paper/2005.14672/) (2020)
- [Sequence-to-Nuggets: Nested Entity Mention Detection via Anchor-Region Networks](https://mlsanity.com/paper/1906.03783/) (2019)
- [NNE: A Dataset for Nested Named Entity Recognition in English Newswire](https://mlsanity.com/paper/1906.01359/) (2019)
- [Neural Cross-Lingual Transfer and Limited Annotated Data for Named Entity Recognition in Danish](https://mlsanity.com/paper/2003.02931/) (2020)
- [An Empirical Study of Pre-trained Transformers for Arabic Information Extraction](https://mlsanity.com/paper/2004.14519/) (2020)
- [Experiments to Improve Named Entity Recognition on Turkish Tweets](https://mlsanity.com/paper/1410.8668/) (2014)

## Built on by (most cited first, 19 of 19)

- [Universal NER: A Gold-Standard Multilingual Named Entity Recognition Benchmark](https://mlsanity.com/paper/2311.09122/) (2023)
- [Exploring Predictive Uncertainty and Calibration in NLP: A Study on the Impact of Method & Data Scarcity](https://mlsanity.com/paper/2210.15452/) (2022)
- [NEREL: A Russian Dataset with Nested Named Entities, Relations and Events](https://mlsanity.com/paper/2108.13112/) (2021)
- [Experimental Standards for Deep Learning in Natural Language Processing Research](https://mlsanity.com/paper/2204.06251/) (2022)
- [NEREL-BIO: A Dataset of Biomedical Abstracts Annotated with Nested Named Entities](https://mlsanity.com/paper/2210.11913/) (2022)
- [RuNNE-2022 Shared Task: Recognizing Nested Named Entities](https://mlsanity.com/paper/2205.11159/) (2022)
- [Sebastian, Basti, Wastl?! Recognizing Named Entities in Bavarian Dialectal Data](https://mlsanity.com/paper/2403.12749/) (2024)
- [Sequence-to-Sequence Lexical Normalization with Multilingual Transformers](https://mlsanity.com/paper/2110.02869/) (2021)
- ['UFAL at MultiLexNorm 2021: Improving Multilingual Lexical Normalization by Fine-tuning ByT5](https://mlsanity.com/paper/2110.15248/) (2021)
- [Neural Text Normalization for Luxembourgish using Real-Life Variation Data](https://mlsanity.com/paper/2412.09383/) (2024)
- [Different Tastes of Entities: Investigating Human Label Variation in Named Entity Annotations](https://mlsanity.com/paper/2402.01423/) (2024)
- [ViLexNorm: A Lexical Normalization Corpus for Vietnamese Social Media Text](https://mlsanity.com/paper/2401.16403/) (2024)
- [Making Sentence Embeddings Robust to User-Generated Content](https://mlsanity.com/paper/2403.17220/) (2024)
- [A Weakly Supervised Data Labeling Framework for Machine Lexical Normalization in Vietnamese Social Media](https://mlsanity.com/paper/2409.20467/) (2024)
- [Comprehensive Evaluation on Lexical Normalization: Boundary-Aware Approaches for Unsegmented Languages](https://mlsanity.com/paper/2505.22273/) (2025)
- [MultiLexNorm++: A Unified Benchmark and a Generative Model for Lexical Normalization for Asian Languages](https://mlsanity.com/paper/2601.16623/) (2026)
- [DANSK and DaCy 2.6.0: Domain Generalization of Danish Named Entity Recognition](https://mlsanity.com/paper/2402.18209/) (2024)
- [Learning Nested Named Entity Recognition from Flat Annotations](https://mlsanity.com/paper/2603.00840/) (2026)
- [Variation is the Norm: Embracing Sociolinguistics in NLP](https://mlsanity.com/paper/2603.24222/) (2026)

## Abstract (arXiv)

This paper introduces DaN+, a new multi-domain corpus and annotation guidelines for Danish nested named entities (NEs) and lexical normalization to support research on cross-lingual cross-domain learning for a less-resourced language. We empirically assess three strategies to model the two-layer Named Entity Recognition (NER) task. We compare transfer capabilities from German versus in-language annotation from scratch. We examine language-specific versus multilingual BERT, and study the effect of lexical normalization on NER. Our results show that 1) the most robust strategy is multi-task learning which is rivaled by multi-label decoding, 2) BERT-based NER models are sensitive to domain shifts, and 3) in-language BERT and lexical normalization are the most beneficial on the least canonical data. Our results also show that an out-of-domain setup remains challenging, while performance on news plateaus quickly. This highlights the importance of cross-domain evaluation of cross-lingual transfer.