[Mova](https://md.mova.elamur.ai/free/en/) · [this page in HTML](https://mova.elamur.ai/free/en/docs/papers/acl-2020-makarov-semi-supervised-contextual-historical-text/)

[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/) / Semi-supervised Contextual Historical Text Normalization

# Semi-supervised Contextual Historical Text Normalization

**Authors:** Peter Makarov and Simon Clematide · **Year:** 2020 · **Venue:** Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics **Link:** [aclanthology.org/2020.acl-main.650](https://aclanthology.org/2020.acl-main.650/) · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)

[**7** citations](https://openalex.org/works?filter=cites:W3034993603) openalex · 2026-10-18[**5** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/acl-2020-makarov-semi-supervised-contextual-historical-text/) of the 140673 papers whose bibliographies we read[Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics](https://aclanthology.org/volumes/2020.acl-main/)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/2020.acl-main.650/)
- [PDF](https://aclanthology.org/2020.acl-main.650.pdf)
- [DOI 10.18653/v1/2020.acl-main.650](https://doi.org/10.18653/v1/2020.acl-main.650)
- [OpenAlex](https://openalex.org/W3034993603)

**Authors (2)**

1. [Peter Makarov](https://openalex.org/A5089340248) h 10 · 1281 citations · 23 works
2. [Simon Clematide](https://openalex.org/A5073027507) [ORCID](https://orcid.org/0000-0003-1365-0662) · h 21 · 1609 citations · 189 works

Citations and author metrics from openalex (2026-10-18); authors with the same name are not always told apart.

## What it is about

The paper normalises historical text with a simple generative model over characters, combined with a target-side language model that sees the context of each word. The model is trained with expectation-maximisation on unlabelled historical text. Across eight datasets, the error reduction against a character-level SMT baseline is about 19% with 500 labelled tokens and about 3% with 5,000. With no labelled training data, and only a 500-token development set, the best setting reaches 88.4% of the best fully supervised scores reported for the same datasets. Reranking adds about 5% of error reduction and bootstrapping about 14% in total. The English dataset has the largest share of identical spellings, at 75%.

## Ideas

- I1. `en/src/ctx.rs` and `en/src/morph.rs`: a target-side model made of counts. Count modern-word bigrams from the EWT training split in a BTreeMap, and use them to rerank the `archaic` candidates for tools/gutenberg lessons. Measure accuracy on a 500-token dev set with and without the reranking.
- I2. Unlabelled data for the lookup: the in-code dictionary in `en/src/lexgen.rs` gives modern forms without labels. Run a simple EM over archaic-to-modern pairs from the lessons, and measure accuracy at 500, 1,000 and 5,000 tokens against the paper's curve.
- I3. Negative control for context: shuffle the context words in the reranker and expect the gain to disappear. If the gain stays, the reranker is not using context, and the gate should show red.

## For Mova

The chapter is [09. Early Modern and archaic forms](https://md.mova.elamur.ai/free/en/docs/languages/en/09-early-modern-and-archaic/). The code is `en/src/ctx.rs`, `en/src/morph.rs` and `en/src/lexgen.rs`. The paper's English data is Middle English and its licence is not in our records, so check it before any use in a build. The neural parts are not for Mova; the count-based reranker is.

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.18653/v1/2020.acl-main.650](https://doi.org/10.18653/v1/2020.acl-main.650)

```
@inproceedings{makarov2020semi,
  title = {{Semi-supervised Contextual Historical Text Normalization}},
  author = {Makarov, Peter and Clematide, Simon},
  booktitle = {Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
  year = {2020},
  doi = {10.18653/v1/2020.acl-main.650},
  url = {https://doi.org/10.18653/v1/2020.acl-main.650}
}
```

- [← previous Tetra-Tagging: Word-Synchronous Parsing with Linear-Time Inference](https://md.mova.elamur.ai/free/en/docs/papers/acl-2020-kitaev-tetra-tagging-word-synchronous-parsing/)
- [next → Extracting Headless MWEs from Dependency Parse Trees: Parsing, Tagging, and Joint Modeling Approaches](https://md.mova.elamur.ai/free/en/docs/papers/acl-2020-shi-extracting-headless-mwes-dependency-parse/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

## References

54 references, 7 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Alistair Baron and Paul Rayson. 2008. VARD2: A tool for dealing with spelling variation in historical corpora. In Postgraduate conference in corpus linguistics. 2 [https://github.com/ZurichNLP/](https://github.com/ZurichNLP/) acl2020-historical-text-normalization Yoshua Bengio, Réjean Ducharme, Pascal Vincent, and Christian Jauvin. 2003. A neural probabilistic language model. Journal of machine learning research. [↗](https://github.com/ZurichNLP/)
2. Kenneth Heafield, Ivan Pouzyrevsky, Jonathan H Clark, and Philipp Koehn. 2013. Scalable modified kneser-ney language model estimation. In ACL. [find](https://scholar.google.com/scholar?q=Kenneth+Heafield%2C+Ivan+Pouzyrevsky%2C+Jonathan+H+Clark%2C+and+Philipp+Koehn%2E+2013%2E+Scalable+modified+kneser%2Dney+language+model+estimation%2E+In+ACL%2E)
3. Taylor Berg-Kirkpatrick, Alexandre Bouchard-Côté, John DeNero, and Dan Klein. 2010. Painless unsupervised learning with features. In NAACL-HLT. [find](https://scholar.google.com/scholar?q=Taylor+Berg%2DKirkpatrick%2C+Alexandre+Bouchard%2DCo%CC%82te%CC%81%2C+John+DeNero%2C+and+Dan+Klein%2E+2010%2E+Painless+unsupervised+learning+with+features%2E+In+NAACL%2DHLT%2E)
4. Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation, 9(8). [find](https://scholar.google.com/scholar?q=Sepp+Hochreiter+and+Ju%CC%88rgen+Schmidhuber%2E+1997%2E+Long+short%2Dterm+memory%2E+Neural+computation%2C+9%288%29%2E)
5. Taylor Berg-Kirkpatrick, Greg Durrett, and Dan Klein. 2013. Unsupervised transcription of historical documents. In ACL. [find](https://scholar.google.com/scholar?q=Taylor+Berg%2DKirkpatrick%2C+Greg+Durrett%2C+and+Dan+Klein%2E+2013%2E+Unsupervised+transcription+of+historical+documents%2E+In+ACL%2E)
6. Marcel Bollmann. 2012. (semi-)automatic normalization of historical texts using distance measures and the Norma tool. In Second Workshop on Annotation of Corpora for Research in theHumanities (ACRH-2). [find](https://scholar.google.com/scholar?q=Marcel+Bollmann%2E+2012%2E+%28semi%2D%29automatic+normalization+of+historical+texts+using+distance+measures+and+the+Norma+tool%2E+In+Second+Workshop+on+Annotation+of+Corpora+for+Research+in+theHumanities+%28ACRH%2D2%29)
7. Marcel Bollmann. 2018. Normalization of historical texts with neural network models. Sprachwissenschaftliches Institut, Ruhr-Universität. [find](https://scholar.google.com/scholar?q=Marcel+Bollmann%2E+2018%2E+Normalization+of+historical+texts+with+neural+network+models%2E+Sprachwissenschaftliches+Institut%2C+Ruhr%2DUniversita%CC%88t%2E)
8. Marcel Bollmann. 2019. A large-scale comparison of historical text normalization systems. In NAACL-HLT. [find](https://scholar.google.com/scholar?q=Marcel+Bollmann%2E+2019%2E+A+large%2Dscale+comparison+of+historical+text+normalization+systems%2E+In+NAACL%2DHLT%2E) [our card](https://md.mova.elamur.ai/free/en/docs/papers/bollmann-2019-large-scale-comparison-historical-text/)
9. Marcel Bollmann, Joachim Bingel, and Anders Søgaard. 2017. Learning attention for historical text normalization by learning to pronounce. In ACL. [find](https://scholar.google.com/scholar?q=Marcel+Bollmann%2C+Joachim+Bingel%2C+and+Anders+S%C3%B8gaard%2E+2017%2E+Learning+attention+for+historical+text+normalization+by+learning+to+pronounce%2E+In+ACL%2E) [our card](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2017-bollmann-learning-attention-historical-text-normalization/)
10. Marcel Bollmann, Florian Petran, and Stefanie Dipper. 2011. Rule-based normalization of historical texts. In Proceedings of the Workshop on Language Technologies for Digital Humanities and Cultural Heritage (LaTeCH). [find](https://scholar.google.com/scholar?q=Marcel+Bollmann%2C+Florian+Petran%2C+and+Stefanie+Dipper%2E+2011%2E+Rule%2Dbased+normalization+of+historical+texts%2E+In+Proceedings+of+the+Workshop+on+Language+Technologies+for+Digital+Humanities+and+Cultural+He) [our card](https://md.mova.elamur.ai/free/en/docs/papers/bollmann-2011-rule-based-normalization-historical-texts/)
11. Marcel Bollmann, Anders Søgaard, and Joachim Bingel. 2018. Multi-task learning for historical text normalization: Size matters. In Proceedings of the Workshop on Deep Learning Approaches for Low-Resource NLP. [find](https://scholar.google.com/scholar?q=Marcel+Bollmann%2C+Anders+S%C3%B8gaard%2C+and+Joachim+Bingel%2E+2018%2E+Multi%2Dtask+learning+for+historical+text+normalization%3A+Size+matters%2E+In+Proceedings+of+the+Workshop+on+Deep+Learning+Approaches+for+Low%2DResou) [our card](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2018-bollmann-multi-task-learning-historical-text/)
12. Peter F. Brown, Stephen A. Della-Pietra, Vincent J. Della-Pietra, and Robert L. Mercer. 1993. The mathematics of statistical machine translation. Computational Linguistics. [find](https://scholar.google.com/scholar?q=Peter+F%2E+Brown%2C+Stephen+A%2E+Della%2DPietra%2C+Vincent+J%2E+Della%2DPietra%2C+and+Robert+L%2E+Mercer%2E+1993%2E+The+mathematics+of+statistical+machine+translation%2E+Computational+Linguistics%2E)
13. Tomaz Erjavec. 2012. The goo300k corpus of historical Slovene. In LREC. [find](https://scholar.google.com/scholar?q=Tomaz+Erjavec%2E+2012%2E+The+goo300k+corpus+of+historical+Slovene%2E+In+LREC%2E)
14. Izaskun Etxeberria, Iñaki Alegria, Larraitz Uria, and Mans Hulden. 2016. Evaluating the noisy channel model for the normalization of historical texts: Basque, Spanish and Slovene. In LREC. [find](https://scholar.google.com/scholar?q=Izaskun+Etxeberria%2C+In%CC%83aki+Alegria%2C+Larraitz+Uria%2C+and+Mans+Hulden%2E+2016%2E+Evaluating+the+noisy+channel+model+for+the+normalization+of+historical+texts%3A+Basque%2C+Spanish+and+Slovene%2E+In+LREC%2E)
15. Rosemarie Fiebranz, Erik Lindberg, Jonas Lindström, and Maria Ågren. 2011. Making verbs count: the research project ‘Gender and Work’ and its methodology. Scandinavian Economic History Review, 59(3). [find](https://scholar.google.com/scholar?q=Rosemarie+Fiebranz%2C+Erik+Lindberg%2C+Jonas+Lindstro%CC%88m%2C+and+Maria+A%CC%8Agren%2E+2011%2E+Making+verbs+count%3A+the+research+project+%E2%80%98Gender+and+Work%E2%80%99+and+its+methodology%2E+Scandinavian+Economic+History+Review%2C+59%283%29)
16. Dan Garrette, Jason Mielens, and Jason Baldridge. 2013. Real-world semi-supervised learning of pos-taggers for low-resource languages. In ACL. [find](https://scholar.google.com/scholar?q=Dan+Garrette%2C+Jason+Mielens%2C+and+Jason+Baldridge%2E+2013%2E+Real%2Dworld+semi%2Dsupervised+learning+of+pos%2Dtaggers+for+low%2Dresource+languages%2E+In+ACL%2E)
17. Bo Han and Timothy Baldwin. 2011. Lexical normalisation of short text messages: Makn sens a #twitter. In ACL. [find](https://scholar.google.com/scholar?q=Bo+Han+and+Timothy+Baldwin%2E+2011%2E+Lexical+normalisation+of+short+text+messages%3A+Makn+sens+a+%23twitter%2E+In+ACL%2E)
18. Mark Hopkins and Jonathan May. 2011. Tuning as ranking. In EMNLP. [find](https://scholar.google.com/scholar?q=Mark+Hopkins+and+Jonathan+May%2E+2011%2E+Tuning+as+ranking%2E+In+EMNLP%2E)
19. Mans Hulden. 2009. Fast aproximate string matching with finite automata. Procesamiento del lenguaje natural, 43. [find](https://scholar.google.com/scholar?q=Mans+Hulden%2E+2009%2E+Fast+aproximate+string+matching+with+finite+automata%2E+Procesamiento+del+lenguaje+natural%2C+43%2E)
20. Michael I Jordan, Zoubin Ghahramani, Tommi S Jaakkola, and Lawrence K Saul. 1999. An introduction to variational methods for graphical models. Machine learning. [find](https://scholar.google.com/scholar?q=Michael+I+Jordan%2C+Zoubin+Ghahramani%2C+Tommi+S+Jaakkola%2C+and+Lawrence+K+Saul%2E+1999%2E+An+introduction+to+variational+methods+for+graphical+models%2E+Machine+learning%2E)
21. Bryan Jurish. 2010. More than words: Using token context to improve canonicalization of historical german. Journal For Language Technology And Computational Linguistics. [find](https://scholar.google.com/scholar?q=Bryan+Jurish%2E+2010%2E+More+than+words%3A+Using+token+context+to+improve+canonicalization+of+historical+german%2E+Journal+For+Language+Technology+And+Computational+Linguistics%2E)
22. Katharina Kann, Kyunghyun Cho, and Samuel R Bowman. 2019. Towards realistic practices in low-resource natural language processing: The development set. In EMNLP. [find](https://scholar.google.com/scholar?q=Katharina+Kann%2C+Kyunghyun+Cho%2C+and+Samuel+R+Bowman%2E+2019%2E+Towards+realistic+practices+in+low%2Dresource+natural+language+processing%3A+The+development+set%2E+In+EMNLP%2E)
23. Kazuya Kawakami, Chris Dyer, and Phil Blunsom. 2019. Learning to discover, ground and use words with segmental neural language models. In ACL. [find](https://scholar.google.com/scholar?q=Kazuya+Kawakami%2C+Chris+Dyer%2C+and+Phil+Blunsom%2E+2019%2E+Learning+to+discover%2C+ground+and+use+words+with+segmental+neural+language+models%2E+In+ACL%2E)
24. 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 ACL. [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)
25. Lingpeng Kong, Chris Dyer, and Noah A Smith. 2016. Segmental recurrent neural networks. In ICLR. [find](https://scholar.google.com/scholar?q=Lingpeng+Kong%2C+Chris+Dyer%2C+and+Noah+A+Smith%2E+2016%2E+Segmental+recurrent+neural+networks%2E+In+ICLR%2E)
26. Natalia Korchagina. 2017. Normalizing medieval german texts: from rules to deep learning. In Proceedings of the NoDaLiDa 2017 Workshop on Processing Historical Language. [find](https://scholar.google.com/scholar?q=Natalia+Korchagina%2E+2017%2E+Normalizing+medieval+german+texts%3A+from+rules+to+deep+learning%2E+In+Proceedings+of+the+NoDaLiDa+2017+Workshop+on+Processing+Historical+Language%2E)
27. Julia Krasselt, Marcel Bollmann, Stefanie Dipper, and Florian Petran. 2015. Guidelines für die Nor-malisierung historischer deutscher Texte / Guidelines for Normalizing Historical German Texts. Bochumer Linguistische Arbeitsberichte: 15. [find](https://scholar.google.com/scholar?q=Julia+Krasselt%2C+Marcel+Bollmann%2C+Stefanie+Dipper%2C+and+Florian+Petran%2E+2015%2E+Guidelines+fu%CC%88r+die+Nor%2Dmalisierung+historischer+deutscher+Texte+%2F+Guidelines+for+Normalizing+Historical+German+Texts%2E+Bochu)
28. Vladimir I Levenshtein. 1966. Binary codes capable of correcting deletions, insertions, and reversals. Soviet physics doklady, 10(8). [find](https://scholar.google.com/scholar?q=Vladimir+I+Levenshtein%2E+1966%2E+Binary+codes+capable+of+correcting+deletions%2C+insertions%2C+and+reversals%2E+Soviet+physics+doklady%2C+10%288%29%2E)
29. Nikola Ljubešić, Katja Zupan, Darja Fišer, and Tomaž Erjavec. 2016. Normalising Slovene data: historical texts vs. user-generated content. In KONVENS. [find](https://scholar.google.com/scholar?q=Nikola+Ljubes%CC%8Cic%CC%81%2C+Katja+Zupan%2C+Darja+Fis%CC%8Cer%2C+and+Tomaz%CC%8C+Erjavec%2E+2016%2E+Normalising+Slovene+data%3A+historical+texts+vs%2E+user%2Dgenerated+content%2E+In+KONVENS%2E)
30. Massimo Lusetti, Tatyana Ruzsics, Anne Göhring, Tanja Samardžić, and Elisabeth Stark. 2018. Encoder-decoder methods for text normalization. In Proceedings of the Fifth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial). [find](https://scholar.google.com/scholar?q=Massimo+Lusetti%2C+Tatyana+Ruzsics%2C+Anne+Go%CC%88hring%2C+Tanja+Samardz%CC%8Cic%CC%81%2C+and+Elisabeth+Stark%2E+2018%2E+Encoder%2Ddecoder+methods+for+text+normalization%2E+In+Proceedings+of+the+Fifth+Workshop+on+NLP+for+Similar+L)
31. Peter Makarov and Simon Clematide. 2018. Neural transition-based string transduction for limited-resource setting in morphology. In COLING. In Language and Technology Conference: Human Language Technologies as a Challenge for Computer Science and Linguistics. [find](https://scholar.google.com/scholar?q=Peter+Makarov+and+Simon+Clematide%2E+2018%2E+Neural+transition%2Dbased+string+transduction+for+limited%2Dresource+setting+in+morphology%2E+In+COLING%2E+In+Language+and+Technology+Conference%3A+Human+Language+Techno)
32. Manfred Markus. 1999. Manual of ICAMET (Innsbruck Computer Archive of Machine-Readable English Texts). Leopold-Franzens-Universitat Innsbruck. [find](https://scholar.google.com/scholar?q=Manfred+Markus%2E+1999%2E+Manual+of+ICAMET+%28Innsbruck+Computer+Archive+of+Machine%2DReadable+English+Texts%29%2E+Leopold%2DFranzens%2DUniversitat+Innsbruck%2E)
33. Rajhans Samdani, Ming-Wei Chang, and Dan Roth. 2012. Unified expectation maximization. In NAACL-HLT. [find](https://scholar.google.com/scholar?q=Rajhans+Samdani%2C+Ming%2DWei+Chang%2C+and+Dan+Roth%2E+2012%2E+Unified+expectation+maximization%2E+In+NAACL%2DHLT%2E)
34. Tomáš Mikolov. 2012. Statistical language models based on neural networks. Ph.D. thesis, Brno University of Technology, Czech Republic. [find](https://scholar.google.com/scholar?q=Toma%CC%81s%CC%8C+Mikolov%2E+2012%2E+Statistical+language+models+based+on+neural+networks%2E+Ph%2ED%2E+thesis%2C+Brno+University+of+Technology%2C+Czech+Republic%2E)
35. Yves Scherrer and Nikola Ljubešić. 2016. Automatic normalisation of the Swiss German ArchiMob corpus using character-level machine translation. In KONVENS. [find](https://scholar.google.com/scholar?q=Yves+Scherrer+and+Nikola+Ljubes%CC%8Cic%CC%81%2E+2016%2E+Automatic+normalisation+of+the+Swiss+German+ArchiMob+corpus+using+character%2Dlevel+machine+translation%2E+In+KONVENS%2E)
36. Mehryar Mohri. 1997. Finite-state transducers in language and speech processing. Computational linguistics. [find](https://scholar.google.com/scholar?q=Mehryar+Mohri%2E+1997%2E+Finite%2Dstate+transducers+in+language+and+speech+processing%2E+Computational+linguistics%2E)
37. Claude Elwood Shannon. 1948. A mathematical theory of communication. Bell system technical journal. [find](https://scholar.google.com/scholar?q=Claude+Elwood+Shannon%2E+1948%2E+A+mathematical+theory+of+communication%2E+Bell+system+technical+journal%2E)
38. Radford M Neal and Geoffrey E Hinton. 1998. A view of the em algorithm that justifies incremental, sparse, and other variants. In Learning in graphical models. Springer. [find](https://scholar.google.com/scholar?q=Radford+M+Neal+and+Geoffrey+E+Hinton%2E+1998%2E+A+view+of+the+em+algorithm+that+justifies+incremental%2C+sparse%2C+and+other+variants%2E+In+Learning+in+graphical+models%2E+Springer%2E)
39. Eszter Simon. 2014. Corpus building from Old Hungarian codices. In The evolution of functional left peripheries in Hungarian syntax. [find](https://scholar.google.com/scholar?q=Eszter+Simon%2E+2014%2E+Corpus+building+from+Old+Hungarian+codices%2E+In+The+evolution+of+functional+left+peripheries+in+Hungarian+syntax%2E)
40. Franz Josef Och. 2003. Minimum error rate training in statistical machine translation. In ACL. [find](https://scholar.google.com/scholar?q=Franz+Josef+Och%2E+2003%2E+Minimum+error+rate+training+in+statistical+machine+translation%2E+In+ACL%2E)
41. Carolin Odebrecht, Malte Belz, Amir Zeldes, Anke Lüdeling, and Thomas Krause. 2017. RIDGES Her-bology: designing a diachronic multi-layer corpus. Language Resources and Evaluation, 51(3). [find](https://scholar.google.com/scholar?q=Carolin+Odebrecht%2C+Malte+Belz%2C+Amir+Zeldes%2C+Anke+Lu%CC%88deling%2C+and+Thomas+Krause%2E+2017%2E+RIDGES+Her%2Dbology%3A+designing+a+diachronic+multi%2Dlayer+corpus%2E+Language+Resources+and+Evaluation%2C+51%283%29%2E)
42. Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al. 2011. Scikit-learn: Machine learning in Python. Journal of machine learning research. [find](https://scholar.google.com/scholar?q=Fabian+Pedregosa%2C+Gae%CC%88l+Varoquaux%2C+Alexandre+Gramfort%2C+Vincent+Michel%2C+Bertrand+Thirion%2C+Olivier+Grisel%2C+Mathieu+Blondel%2C+Peter+Prettenhofer%2C+Ron+Weiss%2C+Vincent+Dubourg%2C+et+al%2E+2011%2E+Scikit%2Dlearn%3A+Mac)
43. Eva Pettersson. 2016. Spelling normalisation and linguistic analysis of historical text for information extraction. Ph.D. thesis, Acta Universitatis Upsaliensis. [find](https://scholar.google.com/scholar?q=Eva+Pettersson%2E+2016%2E+Spelling+normalisation+and+linguistic+analysis+of+historical+text+for+information+extraction%2E+Ph%2ED%2E+thesis%2C+Acta+Universitatis+Upsaliensis%2E)
44. Eva Pettersson, Beáta Megyesi, and Joakim Nivre. 2013a. Normalisation of historical text using context-sensitive weighted Levenshtein distance and compound splitting. In NODALIDA. [find](https://scholar.google.com/scholar?q=Eva+Pettersson%2C+Bea%CC%81ta+Megyesi%2C+and+Joakim+Nivre%2E+2013a%2E+Normalisation+of+historical+text+using+context%2Dsensitive+weighted+Levenshtein+distance+and+compound+splitting%2E+In+NODALIDA%2E) [our card](https://md.mova.elamur.ai/free/en/docs/papers/pettersson-2013-normalisation-historical-text-context-sensitive/)
45. Eva Pettersson, Beáta Megyesi, and Jörg Tiedemann. 2013b. An SMT approach to automatic annotation of historical text. In Proceedings of the Nodalida Workshop on Computational Historical Linguistics. [find](https://scholar.google.com/scholar?q=Eva+Pettersson%2C+Bea%CC%81ta+Megyesi%2C+and+Jo%CC%88rg+Tiedemann%2E+2013b%2E+An+SMT+approach+to+automatic+annotation+of+historical+text%2E+In+Proceedings+of+the+Nodalida+Workshop+on+Computational+Historical+Linguistics%2E)
46. Sujith Ravi and Kevin Knight. 2011. Deciphering foreign language. In ACL-HLT. [find](https://scholar.google.com/scholar?q=Sujith+Ravi+and+Kevin+Knight%2E+2011%2E+Deciphering+foreign+language%2E+In+ACL%2DHLT%2E)
47. Alexander Robertson and Sharon Goldwater. 2018. Evaluating historical text normalization systems: How well do they generalize? In NAACL. [find](https://scholar.google.com/scholar?q=Alexander+Robertson+and+Sharon+Goldwater%2E+2018%2E+Evaluating+historical+text+normalization+systems%3A+How+well+do+they+generalize%3F+In+NAACL%2E) [our card](https://md.mova.elamur.ai/free/en/docs/papers/robertson-2018-evaluating-historical-text-normalization-systems/)
48. Eirı́kur Rögnvaldsson, Anton Karl Ingason, Einar Freyr Sigurdhsson, and Joel Wallenberg. 2012. The icelandic parsed historical corpus (icepahc). In LREC. [find](https://scholar.google.com/scholar?q=Eir%C4%B1%CC%81kur+Ro%CC%88gnvaldsson%2C+Anton+Karl+Ingason%2C+Einar+Freyr+Sigurdhsson%2C+and+Joel+Wallenberg%2E+2012%2E+The+icelandic+parsed+historical+corpus+%28icepahc%29%2E+In+LREC%2E) [our card](https://md.mova.elamur.ai/free/en/docs/papers/rognvaldsson-2012-icelandic-parsed-historical-corpus-icepahc/)
49. Tanja Samardžić, Yves Scherrer, and Elvira Glaser. 2015. Normalising orthographic and dialectal variants for the automatic processing of swiss german. [find](https://scholar.google.com/scholar?q=Tanja+Samardz%CC%8Cic%CC%81%2C+Yves+Scherrer%2C+and+Elvira+Glaser%2E+2015%2E+Normalising+orthographic+and+dialectal+variants+for+the+automatic+processing+of+swiss+german%2E)
50. Valentin I Spitkovsky, Hiyan Alshawi, Daniel Jurafsky, and Christopher D Manning. 2010. Viterbi training improves unsupervised dependency parsing. In CoNLL. [find](https://scholar.google.com/scholar?q=Valentin+I+Spitkovsky%2C+Hiyan+Alshawi%2C+Daniel+Jurafsky%2C+and+Christopher+D+Manning%2E+2010%2E+Viterbi+training+improves+unsupervised+dependency+parsing%2E+In+CoNLL%2E)
51. Gongbo Tang, Fabienne Cap, Eva Pettersson, and Joakim Nivre. 2018. An evaluation of neural machine translation models on historical spelling normalization. In COLING. [find](https://scholar.google.com/scholar?q=Gongbo+Tang%2C+Fabienne+Cap%2C+Eva+Pettersson%2C+and+Joakim+Nivre%2E+2018%2E+An+evaluation+of+neural+machine+translation+models+on+historical+spelling+normalization%2E+In+COLING%2E)
52. Gael Vaamonde. 2017. Userguide for digital edition of texts in PS Post Scriptum. [find](https://scholar.google.com/scholar?q=Gael+Vaamonde%2E+2017%2E+Userguide+for+digital+edition+of+texts+in+PS+Post+Scriptum%2E)
53. Wojciech Zaremba, Ilya Sutskever, and Oriol Vinyals. 2014. Recurrent neural network regularization. arXiv preprint arXiv:1409.2329. [↗](https://arxiv.org/abs/1409.2329)
54. Matthew D Zeiler. 2012. ADADELTA: an adaptive learning rate method. arXiv:1212.5701. [↗](https://arxiv.org/abs/1212.5701)