Benchmarking Multilabel Topic Classification in the Kyrgyz Language

• URL: http://arxiv.org/abs/2308.15952v1
• Date: Wed, 30 Aug 2023 11:02:26 GMT
• Title: Benchmarking Multilabel Topic Classification in the Kyrgyz Language
• Authors: Anton Alekseev, Sergey I. Nikolenko, Gulnara Kabaeva
• Abstract summary: We present a new public benchmark for topic classification in Kyrgyz based on collected and annotated data from the news site 24.KG. We train and evaluate both classical statistical and neural models, reporting the scores, discussing the results, and proposing directions for future work.
• Score: 6.15353988889181
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Kyrgyz is a very underrepresented language in terms of modern natural language processing resources. In this work, we present a new public benchmark for topic classification in Kyrgyz, introducing a dataset based on collected and annotated data from the news site 24.KG and presenting several baseline models for news classification in the multilabel setting. We train and evaluate both classical statistical and neural models, reporting the scores, discussing the results, and proposing directions for future work.

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