# A Little Annotation does a Lot of Good: A Study in Bootstrapping Low-resource Named Entity Recognizers

arXiv: https://arxiv.org/abs/1908.08983 · HTML: https://mlsanity.com/paper/1908.08983/

## Key facts

- Cited by 9 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 23 Aug 2019.

## Built on by (most cited first, 9 of 9)

- [Uncertainty in Natural Language Processing: Sources, Quantification, and Applications](https://mlsanity.com/paper/2306.04459/) (2023)
- [A Survey of Active Learning for Natural Language Processing](https://mlsanity.com/paper/2210.10109/) (2022)
- ["Diversity and Uncertainty in Moderation" are the Key to Data Selection for Multilingual Few-shot Transfer](https://mlsanity.com/paper/2206.15010/) (2022)
- [Less than One-shot: Named Entity Recognition via Extremely Weak Supervision](https://mlsanity.com/paper/2311.02861/) (2023)
- [A Robust and Domain-Adaptive Approach for Low-Resource Named Entity Recognition](https://mlsanity.com/paper/2101.00388/) (2021)
- [Data-efficient Active Learning for Structured Prediction with Partial Annotation and Self-Training](https://mlsanity.com/paper/2305.12634/) (2023)
- [Scoping Review of Active Learning Strategies and their Evaluation Environments for Entity Recognition Tasks](https://mlsanity.com/paper/2407.03895/) (2024)
- [DeMuX: Data-efficient Multilingual Learning](https://mlsanity.com/paper/2311.06379/) (2023)
- [Cross-lingual German Biomedical Information Extraction: from Zero-shot to Human-in-the-Loop](https://mlsanity.com/paper/2301.09908/) (2023)

## Abstract (arXiv)

Most state-of-the-art models for named entity recognition (NER) rely on the availability of large amounts of labeled data, making them challenging to extend to new, lower-resourced languages. However, there are now several proposed approaches involving either cross-lingual transfer learning, which learns from other highly resourced languages, or active learning, which efficiently selects effective training data based on model predictions. This paper poses the question: given this recent progress, and limited human annotation, what is the most effective method for efficiently creating high-quality entity recognizers in under-resourced languages? Based on extensive experimentation using both simulated and real human annotation, we find a dual-strategy approach best, starting with a cross-lingual transferred model, then performing targeted annotation of only uncertain entity spans in the target language, minimizing annotator effort. Results demonstrate that cross-lingual transfer is a powerful tool when very little data can be annotated, but an entity-targeted annotation strategy can achieve competitive accuracy quickly, with just one-tenth of training data.