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

2026-07-23

AI Summary

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.

Abstract

Slang is a central component of everyday language, reflecting linguistic creativity, social identity, and cultural change, yet its dy- namic and non-standard nature makes it difficult to model computationally. We present the first large-scale computational study of slang.gr, a crowdsourced lexicon of Greek non-standard language, combining lexical content, user-generated tags, and interaction data. To enable the systematic analysis, we map noisy folksonomic tags to a structured multi-layer taxonomy capturing both semantic categories and sociolinguistic metadata. Using this representation, we analyze the linguistic structure of Greek slang and the behavior of its contributor community. We find that slang is strongly centered on person-related and evaluative language, exhibits high morphological creativity, and is shaped by highly skewed participation with short user lifespans and overlapping communities. Building on these signals, we introduce a community-based confidence score for definitions that integrates user roles, interaction patterns, and moderation signals. Our results show that taxonomy-based representations improve interpretability while retaining meaningful aspects of behavioral structure, enabling a more structured and interpretable analysis of confidence signals. Overall, this work establishes slang.gr as a computational resource for non-standard Greek and provides a foundation for sociolinguistic NLP, bias analysis, and the study of informal language in LLMs.

Problem

• slang
• non-standard language
• computational modeling
• sociolinguistics
• crowdsourced lexicon

Innovation

• crowdsourced lexicon
• structured taxonomy
• sociolinguistic NLP
• community-based confidence scoring
• non-standard language