Automatic morphological tagging for Ossetic based on data from the Corpus of Oral Texts
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Abstract
In this work we present the first morphologically annotated corpus for Iron Ossetic that conforms to the Universal Dependencies schema. The corpus includes 5454 manually annotated sentences from the Iron Ossetic Corpus of Oral Texts, containing 74032 tokens. We use this corpus to train a BERT-based morphological analyzer. The analyzer achieves tag accuracy of 95.60%. Furthermore, the paper presents the results of experiments aimed at improving classification quality. It is shown that neither filtering the model’s output using a context‑free analyzer nor multi‑task approach lead to a significant improvement. Finally, the paper presents the results of testing the analyzer on out‑of‑domain data and shows that the model achieves tag accuracy of 91.45% on them.
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References
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