Benchmarking Multilabel Topic Classification in the Kyrgyz Language
Anton Alekseev Sergey I. Nikolenko Gulnara Kabaeva
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.