AlbNews: A Corpus of Headlines for Topic Modeling in Albanian
This provides a resource for topic modeling research in Albanian, a low-resource language, but is incremental as it primarily offers a new dataset with baseline results.
The authors tackled the scarcity of Albanian text corpora by introducing AlbNews, a dataset of 600 labeled and 2600 unlabeled news headlines, and found that basic machine learning classifiers outperformed ensemble methods in initial classification scores.
The scarcity of available text corpora for low-resource languages like Albanian is a serious hurdle for research in natural language processing tasks. This paper introduces AlbNews, a collection of 600 topically labeled news headlines and 2600 unlabeled ones in Albanian. The data can be freely used for conducting topic modeling research. We report the initial classification scores of some traditional machine learning classifiers trained with the AlbNews samples. These results show that basic models outrun the ensemble learning ones and can serve as a baseline for future experiments.