---
title: "An Analysis of Simple Data Augmentation for Named — Dai 2020"
description: "Data augmentation is well studied for sentence-level NLP tasks, but it is harder to apply to named entity recognition (NER)."
authors: ["Xiang Dai", "Heike Adel"]
year: 2020
venue: "Proceedings of the 28th International Conference on Computational Linguistics"
source: "https://aclanthology.org/2020.coling-main.343/"
doi: "10.18653/v1/2020.coling-main.343"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/dai-2020-analysis-simple-data-augmentation-named/"
---
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# An Analysis of Simple Data Augmentation for Named Entity Recognition

**Authors:** Xiang Dai, [Heike Adel](https://md.research.elamur.ai/free/en/authors/heike-adel/) · **Year:** 2020 · **Venue:** Proceedings of the 28th International Conference on Computational Linguistics · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2020.coling-main.343/) · [PDF](https://aclanthology.org/2020.coling-main.343.pdf) · [DOI 10.18653/v1/2020.coling-main.343](https://doi.org/10.18653/v1/2020.coling-main.343) · [OpenAlex](https://openalex.org/W3115908473)

**Citations:** 184 citations (openalex-snapshot · 2026-10-14) · [80](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
Data augmentation is well studied for sentence-level NLP tasks, but it is harder to apply to named entity recognition (NER). NER assigns a label to every token, so changing the tokens can make the labels wrong. Dai and Adel adapt simple, model-free augmentation ideas to this token-level setting. They propose four transformations:
- label-wise token replacement (LwTR),
- synonym replacement (SR) via WordNet,
- mention replacement (MR),
- shuffling within same-label segments (SiS).

They also test a combination of all four. None of these needs an externally trained generator, which matters in specialised domains where such models are scarce.

The methods are evaluated on two English domain-specific corpora, MaSciP (materials science) and i2b2-2010 (clinical). Each is tested with full training data and with simulated low-resource subsets. The backbones are a BiLSTM-CRF and a fine-tuned SciBERT-CRF.

Every augmentation improves on the no-augmentation baseline on average. The largest benefit comes with very small training sets, and the combined approach is usually best. With complete training data, gains shrink or turn negative. Augmentation still helps a strong pretrained transformer, so cheap augmentation is a practical first step for low-resource, domain-specific NER.

## Key points
- **Training subsets:** small, medium and large sets were built from the first 50, 150 and 500 training sentences containing a mention, plus the full set. Augmentation was applied only to training data.
- **Datasets:** MaSciP has 21 entity types (e.g., Material, Synthesis-Apparatus). i2b2-2010 has 3 types: Problem, Treatment and Test.
- **Gains on the 50-sentence i2b2-2010 set:**
  - BiLSTM-CRF F1 rose from 17.1 to 26.9 with all methods combined.
  - SciBERT F1 rose from 35.1 to 42.3 with SR.
- **Average improvement across settings (combined methods):** about 3.3 F1 for recurrent models and 1.8 for transformers. MR was the strongest single method for the LSTM, SR for SciBERT.
- **Tuning:** two hyperparameters were grid-searched on the dev set:
  - number of augmented copies per instance: {1, 3, 6, 10}, or {1, 2, 3} when combining methods;
  - replacement probability p: 0.1–0.7.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

Topics: [Named entity recognition](https://md.research.elamur.ai/free/en/topics/named-entity-recognition/)

## Cited by

Cited by

80 papers of our library cite it (of the 140884 whose bibliographies we read); 5 of them have a page here, 75 not yet.

- [NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation](https://md.research.elamur.ai/free/en/papers/dhole-2023-nl-augmenter-framework-task-sensitive/)
- [A Survey of Data Augmentation Approaches for NLP](https://md.research.elamur.ai/free/en/papers/feng-2021-survey-data-augmentation-approaches-nlp/)
- [A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios](https://md.research.elamur.ai/free/en/papers/hedderich-2021-survey-recent-approaches-natural-language/)
- [Wojood: Nested Arabic Named Entity Corpus and Recognition using BERT](https://md.research.elamur.ai/free/en/papers/jarrar-2022-wojood-nested-arabic-named-entity/)
- [Data Augmentation using Transformers and Similarity Measures for Improving Arabic Text Classification](https://md.research.elamur.ai/free/en/papers/refai-2022-data-augmentation-using-transformers-similarity/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{dai2020analysis,
  title = {{An Analysis of Simple Data Augmentation for Named Entity Recognition}},
  author = {Dai, Xiang and Adel, Heike},
  booktitle = {Proceedings of the 28th International Conference on Computational Linguistics},
  year = {2020},
  doi = {10.18653/v1/2020.coling-main.343},
  url = {https://doi.org/10.18653/v1/2020.coling-main.343}
}
```

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