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# A Large-Scale Comparison of Historical Text Normalization Systems

**Authors:** [Marcel Bollmann](https://md.mova.elamur.ai/free/en/docs/papers/authors/marcel-bollmann/) · **Year:** 2019 · **Venue:** Proceedings of NAACL-HLT 2019 (Long and Short Papers) **Link:** [aclanthology.org/N19-1389](https://aclanthology.org/N19-1389/) · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)

[**65** citations](https://openalex.org/works?filter=cites:W2950176361) openalex · 2026-10-19[**48** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bollmann-2019-large-scale-comparison-historical-text/) of the 140673 papers whose bibliographies we read[Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)](https://aclanthology.org/volumes/N19-1/)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/N19-1389/)
- [PDF](https://aclanthology.org/N19-1389.pdf)
- [DOI 10.18653/v1/n19-1389](https://doi.org/10.18653/v1/n19-1389)
- [OpenAlex](https://openalex.org/W2950176361)

**Authors (1)**

1. [Bollmann, Marcel](https://openalex.org/A5005438563) [ORCID](https://orcid.org/0000-0003-2598-8150) · [Semantic Scholar](https://www.semanticscholar.org/author/34887843) · h 11 · 337 citations · 42 works

Citations and author metrics from openalex (2026-10-19); authors with the same name are not always told apart.

## What it is about

The paper compares historical spelling normalization systems across eight languages, covering rule-based, distance-based, character statistical MT and neural approaches. Each system is trained on random subsets from 100 to 50,000 tokens, with up to ten splits per size, so the learning curves show the amount of data each method needs. Three evaluations are reported. Word accuracy on all tokens. Character error rate on the incorrect predictions only, which shows how far off a wrong answer is. Stemming accuracy on the incorrect predictions, which shows whether a wrong answer still has the right stem. The findings: the rule-based Norma tool is best with under about 500 tokens of training data; character-based SMT is best above that; neural models need more data than the corpora provide. Memorizing the most frequent normalization for seen words beats the learned models on those words, so the learned models should be used only for unseen forms.

## Ideas

- I1. Use the learning-curve protocol for each author. Train each part of the translator on 100, 500, 1,000, 5,000 and so on verse pairs, average over up to ten splits, and read off the data each author delta needs before it helps.
- I2. Add stemming accuracy to the error classes of exact mode. An output that is wrong but has the right stem is an inflection error; one with a different stem is a lexical error. The English Snowball stemmer is available for this.
- I3. Route only unseen concepts to `morph`. The standard encoder already uses the most frequent chunk, so measure its accuracy on seen and unseen concepts per author and send to the morphological fallback only the unseen ones.
- I4. Report character error rate on incorrect outputs only, to complement exact match in exact mode, since the paper shows that a wrong output close to the target is a different kind of failure.

## For Mova

The code is `tools/translate` (`measure.rs` for the new measures and `profile.rs` for the seen/unseen split). The paper's neural systems are not for Mova, but its evaluation design is. It supports open problem 2 (keep only merges that lower the residual) and gives the learning-curve protocol that open problem 6 needs before an author delta is trusted.

Topics: [Machine translation](https://md.mova.elamur.ai/free/en/docs/papers/topics/machine-translation/)

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.18653/v1/n19-1389](https://doi.org/10.18653/v1/n19-1389)

```
@inproceedings{bollmann2019large,
  title = {{A Large-Scale Comparison of Historical Text Normalization Systems}},
  author = {Bollmann, Marcel},
  booktitle = {Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)},
  year = {2019},
  doi = {10.18653/v1/n19-1389},
  url = {https://doi.org/10.18653/v1/n19-1389}
}
```

- [← previous Rule-Based Normalization of Historical Texts](https://md.mova.elamur.ai/free/en/docs/papers/bollmann-2011-rule-based-normalization-historical-texts/)
- [next → CILI: the Collaborative Interlingual Index](https://md.mova.elamur.ai/free/en/docs/papers/bond-2016-cili-collaborative-interlingual-index/)
- [↑ back to Machine translation](https://md.mova.elamur.ai/free/en/docs/papers/mtrans/)

## References

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