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LLM as a Morphological Disambiguator for Belarusian: A Preliminary Study
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LLM as a Morphological Disambiguator for Belarusian: A Preliminary Study
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.
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