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University of Crete

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Representative Papers

slang.gr as a Large-Scale Crowdsourced Resource for Non-Standard Greek

Jul 23, 2026

This study addresses the scarcity of structured computational resources for non-standard Greek slang—a linguistic variety hindered by its dynamic and unregulated nature, which impedes both linguistic inquiry and NLP applications. To bridge this gap, the authors present slang.gr, the first large-scale computational resource for Greek slang, integrating lexical entries, user-generated tags, and interaction data. They introduce a novel multi-layer taxonomy that uniquely combines semantic and sociolinguistic metadata. Through folksonomy-based tag cleaning, ontology mapping, community behavior modeling, and a credibility-weighted scoring algorithm, the work reveals that Greek slang is highly oriented toward person- and evaluation-related expressions and exhibits strong morphological innovativeness. The proposed taxonomy significantly enhances analytical interpretability and establishes a foundational framework for computational modeling of non-standard language varieties.

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Latest Papers

slang.gr as a Large-Scale Crowdsourced Resource for Non-Standard Greek

Jul 23, 2026

This study addresses the scarcity of structured computational resources for non-standard Greek slang—a linguistic variety hindered by its dynamic and unregulated nature, which impedes both linguistic inquiry and NLP applications. To bridge this gap, the authors present slang.gr, the first large-scale computational resource for Greek slang, integrating lexical entries, user-generated tags, and interaction data. They introduce a novel multi-layer taxonomy that uniquely combines semantic and sociolinguistic metadata. Through folksonomy-based tag cleaning, ontology mapping, community behavior modeling, and a credibility-weighted scoring algorithm, the work reveals that Greek slang is highly oriented toward person- and evaluation-related expressions and exhibits strong morphological innovativeness. The proposed taxonomy significantly enhances analytical interpretability and establishes a foundational framework for computational modeling of non-standard language varieties.

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