Document Type
Conference Proceeding
Publication Date
1-1-2022
Journal / Book Title
Proceedings International Conference on Computational Linguistics Coling
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.
MSU Digital Commons Citation
Laippala, Veronika; Salmela, Anna; Rönnqvist, Samuel; Aji, Alham Fikri; Chang, Li Hsin; Dhifallah, Asma; Goulart, Larissa; Kortelainen, Henna; Pàmies, Marc; Dutra, Deise Prina; Skantsi, Valtteri; Sutawika, Lintang; and Pyysalo, Sampo, "Towards better structured and less noisy Web data: Oscar with Register annotations" (2022). Department of Linguistics Faculty Scholarship and Creative Works. 61.
https://digitalcommons.montclair.edu/linguistics-facpubs/61
Rights
This work is licensed under the Creative Commons Attribution 4.0 (CC-BY) International License.