Ossetic-COT: Designing a morphologically annotated corpus and morphological analyzer for Ossetic

2026-07-06 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, medium

Summary

Anna Shatskikh and Alexey Sorokin introduce Ossetic-COT, the inaugural morphologically annotated corpus for Iron Ossetic, meticulously designed to align with the Universal Dependencies schema. This new resource comprises 5454 manually annotated sentences, extracted from the Iron Ossetic Corpus of Oral Texts, encompassing a total of 74032 tokens. The creation of this corpus addresses a significant gap for this low-resource language, providing a foundational dataset for further computational linguistic research. Utilizing the newly developed corpus, the researchers successfully trained a BERT-based morphological analyzer. This analyzer demonstrated robust performance, achieving a notable tag accuracy of 95.60%, marking a crucial advancement in natural language processing capabilities for Ossetic.

Key takeaway

For NLP engineers and research scientists focused on low-resource languages, this work provides a critical blueprint. You should consider the Ossetic-COT corpus and its BERT-based morphological analyzer as a model for developing foundational linguistic resources. This demonstrates that even for languages like Iron Ossetic, high-accuracy morphological analysis is achievable with dedicated corpus creation, guiding your strategy for similar linguistic challenges.

Key insights

The first morphologically annotated corpus for Iron Ossetic, conforming to Universal Dependencies, enabled a BERT-based analyzer with 95.60% tag accuracy.

Method

Manually annotated 5454 sentences from the Iron Ossetic Corpus of Oral Texts to create a Universal Dependencies-compliant corpus, subsequently used to train a BERT-based morphological analyzer.

In practice

• Utilize the Ossetic-COT corpus.
• Employ the BERT-based analyzer.

Topics

• Iron Ossetic
• Morphological Analysis
• Corpus Annotation
• Universal Dependencies
• BERT Models
• Low-Resource Languages

Code references

• AragonerUA/SampoNLP
• suralk/multiNER

Best for: AI Scientist, NLP Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.