[Mova](https://md.mova.elamur.ai/free/en/) · [this page in HTML](https://mova.elamur.ai/free/en/docs/papers/hamalainen-2018-normalizing-early-english-letters-present/)

[home](https://md.mova.elamur.ai/free/en/) / [docs](https://md.mova.elamur.ai/free/en/docs/) / [papers](https://md.mova.elamur.ai/free/en/docs/papers/) / [English](https://md.mova.elamur.ai/free/en/docs/papers/en/) / Normalizing Early English Letters to Present-day English Spelling

# Normalizing Early English Letters to Present-day English Spelling

**Authors:** Mika Hämäläinen, Tanja Säily, Jack Rueter, Jörg Tiedemann, Eetu Mäkelä · **Year:** 2018 · **Venue:** Proceedings of the Second Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature **Link:** [aclanthology.org/W18-4510](https://aclanthology.org/W18-4510/) · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)

[**17** citations](https://openalex.org/works?filter=cites:W2956146831) openalex-snapshot · 2026-10-14[**12** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/hamalainen-2018-normalizing-early-english-letters-present/) of the 140673 papers whose bibliographies we read[Proceedings of the Second Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature](https://aclanthology.org/volumes/W18-45/)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/W18-4510/)
- [PDF](https://aclanthology.org/W18-4510.pdf)
- [OpenAlex](https://openalex.org/W2956146831)

**Authors (5)**

1. [Mika Hämäläinen](https://openalex.org/A5002214613) [ORCID](https://orcid.org/0000-0001-9315-1278) · [Semantic Scholar](https://www.semanticscholar.org/author/150213555) · h 12 · 567 citations · 158 works
2. [Tanja Säily](https://openalex.org/A5000404764) [ORCID](https://orcid.org/0000-0003-4407-8929) · [Semantic Scholar](https://www.semanticscholar.org/author/3167799) · h 14 · 881 citations · 95 works
3. [Jack Rueter](https://openalex.org/A5063811339) [ORCID](https://orcid.org/0000-0002-3076-7929) · [Semantic Scholar](https://www.semanticscholar.org/author/71677874) · h 10 · 322 citations · 122 works
4. [Jörg Tiedemann](https://openalex.org/A5082417280) [ORCID](https://orcid.org/0000-0003-3065-7989) · [Semantic Scholar](https://www.semanticscholar.org/author/143675545) · h 43 · 7928 citations · 283 works
5. [Eetu Mäkelä](https://openalex.org/A5091329964) [ORCID](https://orcid.org/0000-0002-8366-8414) · [Semantic Scholar](https://www.semanticscholar.org/author/3127191) · h 21 · 2127 citations · 228 works

Citations and author metrics from openalex-snapshot (2026-10-14); authors with the same name are not always told apart.

## What it is about

Early English personal letters, from the 15th to the 19th century, are full of non-standard spellings. The paper normalises them to present-day spelling with five methods: VARD2-style rewrite rules, contextual edit distance, statistical and neural machine translation (with and without period information), and a weighted vote. Each method alone gets a minority of the words right; the best single method is the neural model. The generic test set reaches 67% when any one method is correct. The vote weighted by each method's accuracy gives 45% on the generic set, 56% on the 15th-century set and 22% on the 18th-century set, while a Markov chain over the candidates gives 36%, 40% and 24%. The authors conclude that the period, which they used as a variable, is not a good enough grouping for spelling variation, and that the methods make complementary errors.

## Ideas

- I1. [2. Spelling, print conventions and tokens](https://md.mova.elamur.ai/free/en/docs/languages/en-x-emode/02-spelling-and-tokens/): write the rewrite rules as an ordered table in a deterministic normaliser, and combine them with a dictionary lookup in `en/src/emode.rs`. Weight each rule by its accuracy on dev data, as the paper's weighted vote does. Measure exact match on held-out letters, run twice.
- I2. Report the "at least one method correct" figure as an upper bound before any change, stratified by period. It shows how much a better selector could gain.
- I3. Negative control: shuffle the period labels in the period-aware table and check that the score falls. The paper's period-aware SMT did not help, so the control is worth running.

## For Mova

The chapters are [2. Spelling, print conventions and tokens](https://md.mova.elamur.ai/free/en/docs/languages/en-x-emode/02-spelling-and-tokens/) and [13. Concrete improvements for Mova's parser and lexicon](https://md.mova.elamur.ai/free/en/docs/languages/en-x-emode/13-improvements/). The code is `en/src/emode.rs` (the closed table of Early Modern forms) and `en/src/morph.rs`. Measure: exact-match and lemma accuracy on a held-out letter sample, with the vote weighted and unweighted.

## Cite

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

```
@inproceedings{hamalainen2018normalizing,
  title = {{Normalizing Early English Letters to Present-day English Spelling}},
  author = {Hämäläinen, Mika and Säily, Tanja and Rueter, Jack and Tiedemann, Jörg and Mäkelä, Eetu},
  booktitle = {Proceedings of the Second Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature},
  year = {2018},
  url = {https://aclanthology.org/W18-4510/}
}
```

- [← previous The CoNLL-2009 Shared Task: Syntactic and Semantic Dependencies in Multiple Languages](https://md.mova.elamur.ai/free/en/docs/papers/hajic-2009-conll-2009-shared-task-syntactic/)
- [next → A Tree Adjoining Grammar Analysis of the Syntax and Semantics of It-Clefts](https://md.mova.elamur.ai/free/en/docs/papers/han-2006-tree-adjoining-grammar-analysis-syntax/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

## References

16 references, 1 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Marilisa Amoia and Jose Manuel Martinez. 2013. Using comparable collections of historical texts for building a diachronic dictionary for spelling normalization. In Proceedings of the 7th workshop on language technology for cultural heritage, social sciences, and humanities, pages 84–89. [find](https://scholar.google.com/scholar?q=Marilisa+Amoia+and+Jose+Manuel+Martinez%2E+2013%2E+Using+comparable+collections+of+historical+texts+for+building+a+diachronic+dictionary+for+spelling+normalization%2E+In+Proceedings+of+the+7th+workshop+on+l)
2. Alistair Baron and Paul Rayson. 2008. VARD2: a tool for dealing with spelling variation in historical corpora. In Postgraduate Conference in Corpus Linguistics. Aston University, Birmingham. [find](https://scholar.google.com/scholar?q=Alistair+Baron+and+Paul+Rayson%2E+2008%2E+VARD2%3A+a+tool+for+dealing+with+spelling+variation+in+historical+corpora%2E+In+Postgraduate+Conference+in+Corpus+Linguistics%2E+Aston+University%2C+Birmingham%2E)
3. Kenneth R. Beesley and Lauri Karttunen, 2003. Finite-State Morphology, pages 451–454. Stanford, CA: CSLI Publications. [find](https://scholar.google.com/scholar?q=Kenneth+R%2E+Beesley+and+Lauri+Karttunen%2C+2003%2E+Finite%2DState+Morphology%2C+pages+451%E2%80%93454%2E+Stanford%2C+CA%3A+CSLI+Publications%2E)
4. Steven Bird, Ewan Klein, and Edward Loper. 2009. Natural Language Processing with Python. O’Reilly Media. [find](https://scholar.google.com/scholar?q=Steven+Bird%2C+Ewan+Klein%2C+and+Edward+Loper%2E+2009%2E+Natural+Language+Processing+with+Python%2E+O%E2%80%99Reilly+Media%2E)
5. Marcel Bollmann and Anders Søgaard. 2016. Improving historical spelling normalization with bi-directional LSTMs and multi-task learning. CoRR, abs/1610.07844. [↗](https://arxiv.org/abs/1610.07844) [our card](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2016-bollmann-improving-historical-spelling-normalization-bi/)
6. Philip R Burns. 2013. MorphAdorner v2: A Java library for the morphological adornment of English language texts. Northwestern University, Evanston, IL. [find](https://scholar.google.com/scholar?q=Philip+R+Burns%2E+2013%2E+MorphAdorner+v2%3A+A+Java+library+for+the+morphological+adornment+of+English+language+texts%2E+Northwestern+University%2C+Evanston%2C+IL%2E)
7. Kenneth Heafield, Ivan Pouzyrevsky, Jonathan H. Clark, and Philipp Koehn. 2013. Scalable Modified Kneser-Ney Language Model Estimation. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, pages 690–696, Sofia, Bulgaria, August. [find](https://scholar.google.com/scholar?q=Kenneth+Heafield%2C+Ivan+Pouzyrevsky%2C+Jonathan+H%2E+Clark%2C+and+Philipp+Koehn%2E+2013%2E+Scalable+Modified+Kneser%2DNey+Language+Model+Estimation%2E+In+Proceedings+of+the+51st+Annual+Meeting+of+the+Association+for)
8. Guillaume Klein, Yoon Kim, Yuntian Deng, Jean Senellart, and Alexander M. Rush. 2017. OpenNMT: Open-Source Toolkit for Neural Machine Translation. In Proc. ACL. [find](https://scholar.google.com/scholar?q=Guillaume+Klein%2C+Yoon+Kim%2C+Yuntian+Deng%2C+Jean+Senellart%2C+and+Alexander+M%2E+Rush%2E+2017%2E+OpenNMT%3A+Open%2DSource+Toolkit+for+Neural+Machine+Translation%2E+In+Proc%2E+ACL%2E)
9. Philipp Koehn, Hieu Hoang, Alexandra Birch, Chris Callison-Burch, Marcello Federico, Nicola Bertoldi, Brooke Cowan, Wade Shen, Christine Moran, Richard Zens, et al. 2007. Moses: Open source toolkit for statistical machine translation. In Proceedings of the 45th annual meeting of the ACL on interactive poster and demonstration sessions, pages 177–180. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Philipp+Koehn%2C+Hieu+Hoang%2C+Alexandra+Birch%2C+Chris+Callison%2DBurch%2C+Marcello+Federico%2C+Nicola+Bertoldi%2C+Brooke+Cowan%2C+Wade+Shen%2C+Christine+Moran%2C+Richard+Zens%2C+et+al%2E+2007%2E+Moses%3A+Open+source+toolkit+fo)
10. Krister Lindén, Erik Axelson, Senka Drobac, Sam Hardwick, Juha Kuokkala, Jyrki Niemi, Tommi A. Pirinen, and Miikka Silfverberg. 2013. HFST — A System for Creating NLP Tools. In Cerstin Mahlow and Michael Piotrowski, editors, Systems and Frameworks for Computational Morphology, pages 53–71, Berlin, Heidelberg. Springer Berlin Heidelberg. [find](https://scholar.google.com/scholar?q=Krister+Linde%CC%81n%2C+Erik+Axelson%2C+Senka+Drobac%2C+Sam+Hardwick%2C+Juha+Kuokkala%2C+Jyrki+Niemi%2C+Tommi+A%2E+Pirinen%2C+and+Miikka+Silfverberg%2E+2013%2E+HFST+%E2%80%94+A+System+for+Creating+NLP+Tools%2E+In+Cerstin+Mahlow+and+Mic)
11. MED. n.d. Middle English Dictionary. University of Michigan. [https://quod.lib.umich.edu/m/med/](https://quod.lib.umich.edu/m/med/). Terttu Nevalainen, Helena Raumolin-Brunberg, Jukka Keränen, Minna Nevala, Arja Nurmi, Minna Palander-Collin, Samuli Kaislaniemi, Mikko Laitinen, Tanja Säily, and Anni Sairio. 1998–2006. CEEC, Corpora of Early English Correspondence. Department of Modern Languages, University of Helsinki. http://www.helsinki.fi/varieng/CoRD/corpora/CEEC/. [↗](https://quod.lib.umich.edu/m/med/)
12. Franz Josef Och and Hermann Ney. 2003. A Systematic Comparison of Various Statistical Alignment Models. Computational Linguistics, 29(1):19–51. [↗](https://doi.org/10.1162/089120103321337421)
13. Eva Pettersson, Beáta Megyesi, and Jörg Tiedemann. 2013. An SMT approach to automatic annotation of historical text. In Proceedings of the workshop on computational historical linguistics at NODALIDA 2013; May 22-24; 2013; Oslo; Norway. NEALT Proceedings Series 18, number 087, pages 54–69. Linköping University Electronic Press. [find](https://scholar.google.com/scholar?q=Eva+Pettersson%2C+Bea%CC%81ta+Megyesi%2C+and+Jo%CC%88rg+Tiedemann%2E+2013%2E+An+SMT+approach+to+automatic+annotation+of+historical+text%2E+In+Proceedings+of+the+workshop+on+computational+historical+linguistics+at+NODALID)
14. Tanja Säily. 2018. Test set for https://doi.org/10.5281/zenodo.1300332. Normalization of Historical English in CEEC, June. [↗](https://doi.org/10.5281/zenodo.1300332)
15. Tanja Samardzic, Yves Scherrer, and Elvira Glaser, 2015. Normalising orthographic and dialectal variants for the automatic processing of Swiss German. Proceedings of the 7th Language and Technology Conference. ID: unige:82397. [find](https://scholar.google.com/scholar?q=Tanja+Samardzic%2C+Yves+Scherrer%2C+and+Elvira+Glaser%2C+2015%2E+Normalising+orthographic+and+dialectal+variants+for+the+automatic+processing+of+Swiss+German%2E+Proceedings+of+the+7th+Language+and+Technology+Co)
16. The BNC Consortium. 2007. The British National Corpus, version 3 (BNC XML Edition). Distributed by Bodleian Libraries, University of Oxford, on behalf of the BNC Consortium. [http://www.natcorp.ox.ac.uk/](http://www.natcorp.ox.ac.uk/). [↗](http://www.natcorp.ox.ac.uk/)