Year: 2026
Type: conference-paper
Abstract: We explore the use of large language models (LLMs) for morphological disambiguation in Belarusian, a low-resource language.The pipeline has two stages: a rule-based analyzer generates candidate lemmas and grammatical tags, which an LLM then disambiguates in context.Initial evaluation of ChatGPT, Claude, and Gemini on a gold-standard sample shows high accuracy.We scale this approach to a 375K-word corpus using Gemini and compare the results against a neural baseline (Stanza).Manual review of discrepancies suggests that the LLM-based approach outperforms the baseline, offering a solution for corpus annotation in Belarusian.
Institution: University of Vienna
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166566565
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