# ANEA: Distant Supervision for Low-Resource Named Entity Recognition

arXiv: https://arxiv.org/abs/2102.13129 · HTML: https://mlsanity.com/paper/2102.13129/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 22 references that are in the map.
- First posted on arXiv 25 Feb 2021.

## Builds on (most cited first, 7 of 7)

- [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016)
- [Snorkel: Rapid Training Data Creation with Weak Supervision](https://mlsanity.com/paper/1711.10160/) (2017)
- [Learning Word Vectors for 157 Languages](https://mlsanity.com/paper/1802.06893/) (2018)
- [Distantly Supervised Named Entity Recognition using Positive-Unlabeled Learning](https://mlsanity.com/paper/1906.01378/) (2019)
- [Massive vs. Curated Word Embeddings for Low-Resourced Languages. The Case of Yor`ub'a and Twi](https://mlsanity.com/paper/1912.02481/) (2019)
- [Feature-Dependent Confusion Matrices for Low-Resource NER Labeling with Noisy Labels](https://mlsanity.com/paper/1910.06061/) (2019)
- [A Lightweight Front-end Tool for Interactive Entity Population](https://mlsanity.com/paper/1708.00481/) (2017)

## Built on by (most cited first, 8 of 8)

- [ARAIDA: Analogical Reasoning-Augmented Interactive Data Annotation](https://mlsanity.com/paper/2405.11912/) (2024)
- [Selective Annotation via Data Allocation: These Data Should Be Triaged to Experts for Annotation Rather Than the Model](https://mlsanity.com/paper/2405.12081/) (2024)
- [Learning with Noisy Labels by Adaptive Gradient-Based Outlier Removal](https://mlsanity.com/paper/2306.04502/) (2023)
- [ULF: Unsupervised Labeling Function Correction using Cross-Validation for Weak Supervision](https://mlsanity.com/paper/2204.06863/) (2022)
- [Multilinguals at SemEval-2022 Task 11: Complex NER in Semantically Ambiguous Settings for Low Resource Languages](https://mlsanity.com/paper/2207.06882/) (2022)
- [Multilinguals at SemEval-2022 Task 11: Transformer Based Architecture for Complex NER](https://mlsanity.com/paper/2204.02173/) (2022)
- [How to tackle an emerging topic? Combining strong and weak labels for Covid news NER](https://mlsanity.com/paper/2209.15108/) (2022)
- [Domain Adaptation of Multilingual Semantic Search -- Literature Review](https://mlsanity.com/paper/2402.02932/) (2024)

## Abstract (arXiv)

Distant supervision allows obtaining labeled training corpora for low-resource settings where only limited hand-annotated data exists. However, to be used effectively, the distant supervision must be easy to gather. In this work, we present ANEA, a tool to automatically annotate named entities in texts based on entity lists. It spans the whole pipeline from obtaining the lists to analyzing the errors of the distant supervision. A tuning step allows the user to improve the automatic annotation with their linguistic insights without labelling or checking all tokens manually. In six low-resource scenarios, we show that the F1-score can be increased by on average 18 points through distantly supervised data obtained by ANEA.