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        <title>Normalizing Non-canonical Turkish Texts Using Machine Translation Approaches</title>
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        <namePart type="given">Talha</namePart>
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            <namePart type="given">Fernando</namePart>
            <namePart type="family">Alva-Manchego</namePart>
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            <namePart type="given">Eunsol</namePart>
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    <abstract>With the growth of the social web, user-generated text data has reached unprecedented sizes. Non-canonical text normalization provides a way to exploit this as a practical source of training data for language processing systems. The state of the art in Turkish text normalization is composed of a token level pipeline of modules, heavily dependent on external linguistic resources and manually defined rules. Instead, we propose a fully automated, context-aware machine translation approach with fewer stages of processing. Experiments with various implementations of our approach show that we are able to surpass the current best-performing system by a large margin.</abstract>
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%0 Conference Proceedings
%T Normalizing Non-canonical Turkish Texts Using Machine Translation Approaches
%A Çolakoğlu, Talha
%A Sulubacak, Umut
%A Tantuğ, Ahmet Cüneyd
%Y Alva-Manchego, Fernando
%Y Choi, Eunsol
%Y Khashabi, Daniel
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F colakoglu-etal-2019-normalizing
%X With the growth of the social web, user-generated text data has reached unprecedented sizes. Non-canonical text normalization provides a way to exploit this as a practical source of training data for language processing systems. The state of the art in Turkish text normalization is composed of a token level pipeline of modules, heavily dependent on external linguistic resources and manually defined rules. Instead, we propose a fully automated, context-aware machine translation approach with fewer stages of processing. Experiments with various implementations of our approach show that we are able to surpass the current best-performing system by a large margin.
%R 10.18653/v1/P19-2037
%U https://aclanthology.org/P19-2037
%U https://doi.org/10.18653/v1/P19-2037
%P 267-272

Markdown (Informal)

[Normalizing Non-canonical Turkish Texts Using Machine Translation Approaches](https://aclanthology.org/P19-2037) (Çolakoğlu et al., ACL 2019)

• Normalizing Non-canonical Turkish Texts Using Machine Translation Approaches (Çolakoğlu et al., ACL 2019)

ACL

• Talha Çolakoğlu, Umut Sulubacak, and Ahmet Cüneyd Tantuğ. 2019. Normalizing Non-canonical Turkish Texts Using Machine Translation Approaches. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 267–272, Florence, Italy. Association for Computational Linguistics.