Normalizing Early English Letters to Present-day English Spelling
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. languages/en-x-emode/02-spelling-and-tokens.md: 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 languages/en-x-emode/02-spelling-and-tokens.md and languages/en-x-emode/13-improvements.md. 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
@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/}
}References
- 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
- 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
- Kenneth R. Beesley and Lauri Karttunen, 2003. Finite-State Morphology, pages 451–454. Stanford, CA: CSLI Publications. find
- Steven Bird, Ewan Klein, and Edward Loper. 2009. Natural Language Processing with Python. O’Reilly Media. find
- Marcel Bollmann and Anders Søgaard. 2016. Improving historical spelling normalization with bi-directional LSTMs and multi-task learning. CoRR, abs/1610.07844. ↗ our card
- Philip R Burns. 2013. MorphAdorner v2: A Java library for the morphological adornment of English language texts. Northwestern University, Evanston, IL. find
- 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 our card
- 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 our card
- 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
- 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
- MED. n.d. Middle English Dictionary. University of Michigan. 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/. ↗
- Franz Josef Och and Hermann Ney. 2003. A Systematic Comparison of Various Statistical Alignment Models. Computational Linguistics, 29(1):19–51. find
- 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
- Tanja Säily. 2018. Test set for https://doi.org/10.5281/zenodo.1300332. Normalization of Historical English in CEEC, June. ↗
- 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
- 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/. ↗