A Hybrid Protocol for Large-Scale Semantic Dataset Generation in Low-Resource Languages: The Turkish Semantic Relations Corpus

• URL: http://arxiv.org/abs/2601.13253v1
• Date: Mon, 19 Jan 2026 17:38:52 GMT
• Title: A Hybrid Protocol for Large-Scale Semantic Dataset Generation in Low-Resource Languages: The Turkish Semantic Relations Corpus
• Abstract summary: We present a hybrid methodology for generating large-scale semantic relationship datasets in low-resource languages.<n>Our approach integrates FastText embeddings with Agglomerative Clustering to identify semantic clusters, Gemini 2.5-Flash for automated semantic relationship classification, and integration with curated dictionary sources.<n>The resulting dataset comprises 843,000 unique Turkish semantic pairs representing a 10x scale increase over existing resources at minimal cost.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a hybrid methodology for generating large-scale semantic relationship datasets in low-resource languages, demonstrated through a comprehensive Turkish semantic relations corpus. Our approach integrates three phases: (1) FastText embeddings with Agglomerative Clustering to identify semantic clusters, (2) Gemini 2.5-Flash for automated semantic relationship classification, and (3) integration with curated dictionary sources. The resulting dataset comprises 843,000 unique Turkish semantic pairs across three relationship types (synonyms, antonyms, co-hyponyms) representing a 10x scale increase over existing resources at minimal cost ($65). We validate the dataset through two downstream tasks: an embedding model achieving 90% top-1 retrieval accuracy and a classification model attaining 90% F1-macro. Our scalable protocol addresses critical data scarcity in Turkish NLP and demonstrates applicability to other low-resource languages. We publicly release the dataset and models.

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