Towards better structured and less noisy Web data: Oscar with Register annotations

Veronika Laippala, Anna Salmela, Samuel Rönnqvist, Alham Fikri Aji, Li-Hsin Chang, Asma Dhifallah, Larissa Goulart, Henna Kortelainen, Marc Pàmies, Deise Prina Dutra, Valtteri Skantsi, Lintang Sutawika, Sampo Pyysalo

Abstract
Web-crawled datasets are known to be noisy, as they feature a wide range of language use covering both user-generated and professionally edited content as well as noise originating from the crawling process. This article presents one solution to reduce this noise by using automatic register (genre) identification -whether the texts are, e.g., forum discussions, lyrical or how-to pages. We apply the multilingual register identification model by Rönnqvist et al. (2021) and label the widely used Oscar dataset. Additionally, we evaluate the model against eight new languages, showing that the performance is comparable to previous findings on a restricted set of languages. Finally, we present and apply a machine learning method for further cleaning text files originating from Web crawls from remains of boilerplate and other elements not belonging to the main text of the Web page. The register labeled and cleaned dataset covers 351 million documents in 14 languages and is available at https://huggingface.co/datasets/TurkuNLP/register_oscar.
Anthology ID:
2022.wnut-1.23
Volume:
Proceedings of the Eighth Workshop on Noisy User-generated Text (W-NUT 2022)
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
WNUT
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Publisher:
Association for Computational Linguistics
Note:
Pages:
215–221
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URL:
https://aclanthology.org/2022.wnut-1.23
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Cite (ACL):
Veronika Laippala, Anna Salmela, Samuel Rönnqvist, Alham Fikri Aji, Li-Hsin Chang, Asma Dhifallah, Larissa Goulart, Henna Kortelainen, Marc Pàmies, Deise Prina Dutra, Valtteri Skantsi, Lintang Sutawika, and Sampo Pyysalo. 2022. Towards better structured and less noisy Web data: Oscar with Register annotations. In Proceedings of the Eighth Workshop on Noisy User-generated Text (W-NUT 2022), pages 215–221, Gyeongju, Republic of Korea. Association for Computational Linguistics.
Cite (Informal):
Towards better structured and less noisy Web data: Oscar with Register annotations (Laippala et al., WNUT 2022)
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