This article details the data collection and processing methods used to create a language model for Crimean Tatar, a language with limited online resources. The author explains the challenges of working with scarce data, including the use of Turkish language data as a substitute and pseudo-labeling techniques involving two models. The focus is on the metrics, verification methodology, and lessons learned during the data preparation process. AI
IMPACT Provides insights into data acquisition and processing techniques for low-resource languages, potentially aiding future NLP research in similar domains.
RANK_REASON The article details the methodology and data processing for creating a language model for a low-resource language, which falls under research. [lever_c_demoted from research: ic=1 ai=1.0]
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