# Named Entity Recognition with Extremely Limited Data

arXiv: https://arxiv.org/abs/1806.04411 · HTML: https://mlsanity.com/paper/1806.04411/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2018.

## Built on by (most cited first, 2 of 2)

- [AutoTriggER: Label-Efficient and Robust Named Entity Recognition with Auxiliary Trigger Extraction](https://mlsanity.com/paper/2109.04726/) (2021)
- [ANEA: Automated (Named) Entity Annotation for German Domain-Specific Texts](https://mlsanity.com/paper/2112.06724/) (2021)

## Abstract (arXiv)

Traditional information retrieval treats named entity recognition as a pre-indexing corpus annotation task, allowing entity tags to be indexed and used during search. Named entity taggers themselves are typically trained on thousands or tens of thousands of examples labeled by humans. However, there is a long tail of named entities classes, and for these cases, labeled data may be impossible to find or justify financially. We propose exploring named entity recognition as a search task, where the named entity class of interest is a query, and entities of that class are the relevant "documents". What should that query look like? Can we even perform NER-style labeling with tens of labels? This study presents an exploration of CRF-based NER models with handcrafted features and of how we might transform them into search queries.