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

TL;DR

This paper introduces a scalable hybrid methodology for creating large-scale semantic datasets in low-resource languages, exemplified by Turkish, significantly expanding existing resources at low cost and validating through downstream NLP tasks.

Contribution

A novel hybrid protocol combining clustering, automated classification, and dictionary integration for large-scale semantic dataset generation in low-resource languages.

Findings

Dataset of 843,000 semantic pairs created for Turkish

Embedding model achieves 90% top-1 retrieval accuracy

Classification model attains 90% F1-macro score

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.