# Soft Gazetteers for Low-Resource Named Entity Recognition

arXiv: https://arxiv.org/abs/2005.01866 · HTML: https://mlsanity.com/paper/2005.01866/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 May 2020.

## Built on by (most cited first, 20 of 20)

- [MasakhaNER: Named Entity Recognition for African Languages](https://mlsanity.com/paper/2103.11811/) (2021)
- [MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NER](https://mlsanity.com/paper/2108.13655/) (2021)
- [KinyaBERT: a Morphology-aware Kinyarwanda Language Model](https://mlsanity.com/paper/2203.08459/) (2022)
- [The Zeno's Paradox of `Low-Resource' Languages](https://mlsanity.com/paper/2410.20817/) (2024)
- [ACLM: A Selective-Denoising based Generative Data Augmentation Approach for Low-Resource Complex NER](https://mlsanity.com/paper/2306.00928/) (2023)
- [Improved Latent Tree Induction with Distant Supervision via Span Constraints](https://mlsanity.com/paper/2109.05112/) (2021)
- [Making Large Language Models Better Knowledge Miners for Online Marketing with Progressive Prompting Augmentation](https://mlsanity.com/paper/2312.05276/) (2023)
- [ParaNames 1.0: Creating an Entity Name Corpus for 400+ Languages using Wikidata](https://mlsanity.com/paper/2405.09496/) (2024)
- [Unraveling Social Perceptions & Behaviors towards Migrants on Twitter](https://mlsanity.com/paper/2112.06642/) (2021)
- [Can Large Language Models Really Recognize Your Name?](https://mlsanity.com/paper/2505.14549/) (2025)
- [Mining Wikidata for Name Resources for African Languages](https://mlsanity.com/paper/2104.00558/) (2021)
- [USTC-NELSLIP at SemEval-2023 Task 2: Statistical Construction and Dual Adaptation of Gazetteer for Multilingual Complex NER](https://mlsanity.com/paper/2305.02517/) (2023)
- [CebuaNER: A New Baseline Cebuano Named Entity Recognition Model](https://mlsanity.com/paper/2310.00679/) (2023)
- [Rethinking the Value of Gazetteer in Chinese Named Entity Recognition](https://mlsanity.com/paper/2207.02802/) (2022)
- [Multilinguals at SemEval-2022 Task 11: Complex NER in Semantically Ambiguous Settings for Low Resource Languages](https://mlsanity.com/paper/2207.06882/) (2022)
- [PAI at SemEval-2023 Task 2: A Universal System for Named Entity Recognition with External Entity Information](https://mlsanity.com/paper/2305.06099/) (2023)
- [A Multi-way Parallel Named Entity Annotated Corpus for English, Tamil and Sinhala](https://mlsanity.com/paper/2412.02056/) (2024)
- [SegMix: A Simple Structure-Aware Data Augmentation Method](https://mlsanity.com/paper/2311.09505/) (2023)
- [HiligayNER: A Baseline Named Entity Recognition Model for Hiligaynon](https://mlsanity.com/paper/2510.10776/) (2025)
- [HUMAID-NER: A Disaster Tweet Dataset for Joint Named Entity Recognition and Event Classification via Uncertainty-Weighted Multitask Learning](https://mlsanity.com/paper/2609.16964/) (2026)

## Abstract (arXiv)

Traditional named entity recognition models use gazetteers (lists of entities) as features to improve performance. Although modern neural network models do not require such hand-crafted features for strong performance, recent work has demonstrated their utility for named entity recognition on English data. However, designing such features for low-resource languages is challenging, because exhaustive entity gazetteers do not exist in these languages. To address this problem, we propose a method of "soft gazetteers" that incorporates ubiquitously available information from English knowledge bases, such as Wikipedia, into neural named entity recognition models through cross-lingual entity linking. Our experiments on four low-resource languages show an average improvement of 4 points in F1 score. Code and data are available at https://github.com/neulab/soft-gazetteers.