@inproceedings{hamalainen-etal-2018-normalizing,
title = "Normalizing Early {E}nglish Letters to Present-day {E}nglish Spelling",
author = {H{\"a}m{\"a}l{\"a}inen, Mika and
S{\"a}ily, Tanja and
Rueter, Jack and
Tiedemann, J{\"o}rg and
M{\"a}kel{\"a}, Eetu},
editor = "Alex, Beatrice and
Degaetano-Ortlieb, Stefania and
Feldman, Anna and
Kazantseva, Anna and
Reiter, Nils and
Szpakowicz, Stan",
booktitle = "Proceedings of the Second Joint {SIGHUM} Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
month = aug,
year = "2018",
address = "Santa Fe, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-4510",
pages = "87--96",
abstract = "This paper presents multiple methods for normalizing the most deviant and infrequent historical spellings in a corpus consisting of personal correspondence from the 15th to the 19th century. The methods include machine translation (neural and statistical), edit distance and rule-based FST. Different normalization methods are compared and evaluated. All of the methods have their own strengths in word normalization. This calls for finding ways of combining the results from these methods to leverage their individual strengths.",
}
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%0 Conference Proceedings
%T Normalizing Early English Letters to Present-day English Spelling
%A Hämäläinen, Mika
%A Säily, Tanja
%A Rueter, Jack
%A Tiedemann, Jörg
%A Mäkelä, Eetu
%Y Alex, Beatrice
%Y Degaetano-Ortlieb, Stefania
%Y Feldman, Anna
%Y Kazantseva, Anna
%Y Reiter, Nils
%Y Szpakowicz, Stan
%S Proceedings of the Second Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico
%F hamalainen-etal-2018-normalizing
%X This paper presents multiple methods for normalizing the most deviant and infrequent historical spellings in a corpus consisting of personal correspondence from the 15th to the 19th century. The methods include machine translation (neural and statistical), edit distance and rule-based FST. Different normalization methods are compared and evaluated. All of the methods have their own strengths in word normalization. This calls for finding ways of combining the results from these methods to leverage their individual strengths.
%U https://aclanthology.org/W18-4510
%P 87-96
Markdown (Informal)
[Normalizing Early English Letters to Present-day English Spelling](https://aclanthology.org/W18-4510) (Hämäläinen et al., LaTeCH 2018)
ACL
- Mika Hämäläinen, Tanja Säily, Jack Rueter, Jörg Tiedemann, and Eetu Mäkelä. 2018. Normalizing Early English Letters to Present-day English Spelling. In Proceedings of the Second Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, pages 87–96, Santa Fe, New Mexico. Association for Computational Linguistics.