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ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2023
License: CC BY
Data sources: ZENODO
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Tigrinya Dialect Identification (TDI)

Authors: Asfaw Gedamu Haileslasie; Asmelash Teka Hadgu;

Tigrinya Dialect Identification (TDI)

Abstract

The Tigrinya Dialect Identification (TDI) dataset contains text on three Tigrinya dialects or varieties namely: Z, D, and L. The purpose of this dataset is to study dialect identification for Tigrinya using machine learning. For the Z variety, we used snippets from the book ኽልተ ዛንታት (Kilte Zantatat). For the L variety, we used book chapters from ፋቶ (Fato) and ዕርቂ እንደርታ (Erqi Enderta). For the D variant, we could not find a book. Instead, we collected data from two Facebook users, Akeza Awalom and Guraya Asadi Raya that consistently write in that variety. Sentences collected for each dialect were translated to the other dialect with expert native speakers in the target dialect. Source by Dialect Dialect Source No. sentences Z Kilte Zantatat 1041 L Fato 764 Erqi nderta 405 D Akeza Awalom 224 GualRaya 530 Acknowledgements Special thanks to Meles Solomon, who provided us with his book, ዕርቂ እንደርታ (Erqi Enderta). He also helped with translations to the L dialect. Thanks also goes to Tesfay Gebreegziabher and Gidey Gebrekidan for allowing us to use their books Fato and Kilte Zantatat respectively. Many thanks to Teklay Berhane, Abeba Asemu, Haftu Abadi, Tsegay Kinfe, Moges Bekru, Kahsay Berhe Adhana, Kibrom Mulugeta, Tsegazeab Kidanu, Tsgab Weldemariam, Abu W Debay, Solomon Shibabaw, Hagos Hiete for their valuable contributions as translators.

Keywords

tigrinya, dialect identification, variety identification, natural language processing

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This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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