slang.gr as a Large-Scale Crowdsourced Resource for Non-Standard Greek
- URL: http://arxiv.org/abs/2607.21255v1
- Date: Thu, 23 Jul 2026 12:27:18 GMT
- Title: slang.gr as a Large-Scale Crowdsourced Resource for Non-Standard Greek
- Abstract summary: We present the first large-scale computational study of slang.gr, a crowdsourced lexicon of Greek non-standard language.<n>We map noisy folksonomic tags to a structured multi-layer taxonomy capturing both semantic categories and sociolinguistic metadata.<n>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.
- Score: 0.5735035463793009
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
Related papers
- Self-Supervised Lexical Representation Learning for Fast, Large-Scale Phylogenetic Inference [0.0]
This paper introduces a fully self-supervised contrastive learning framework.<n>It learns lexical representations directly from raw IPA-transcribed wordlists.<n>It infers a global phylogenetic tree of 3,399 language varieties.
arXiv Detail & Related papers (2026-09-04T15:23:12Z) - Do Language Models Know Their Slang? Queer Slang Understanding in User-Generated Content [2.174476613068234]
Slang-Q is a dataset of naturally user-generated English sentences paired with queer slang terms and reference definitions.<n>We use this resource to conduct a first exploratory evaluation of language models on their ability to understand and define queer slang.
arXiv Detail & Related papers (2026-08-05T13:40:48Z) - HyperPersona: A Multi-Level Hypergraph Framework for Text-Based Automatic Personality Prediction [0.0]
Text-based Automatic Personality Prediction (APP) seeks to infer personality from linguistic behavior.<n>We propose HyperPersona, a framework that explicitly models the hierarchical organization of text (document, sentence, and word) through hypergraph structure.<n>We show that HyperPersona effectively integrates multi-level linguistic cues, achieving superior performance compared to state-of-the-art baselines.
arXiv Detail & Related papers (2026-05-17T09:49:06Z) - Language, Place, and Social Media: Geographic Dialect Alignment in New Zealand [0.0]
The study examines how language use reflects place identity and patterns of language variation and change based on user-informed lexical, morphosyntactic, and semantic variables.<n>The research involved the creation of a corpus containing 4.26 billion unprocessed words, which offers a valuable resource for future study.
arXiv Detail & Related papers (2026-04-17T06:37:25Z) - Beyond Training for Cultural Awareness: The Role of Dataset Linguistic Structure in Large Language Models [11.682773682018977]
We look at the linguistic properties of fine-tuning datasets used for cultural adaptation.<n>We compute lightweight linguistic, semantic, and structural metrics for Arabic, Chinese, and Japanese datasets.<n>We show that lexical-oriented components (PC3) are the most robust, yielding more consistent performance across models and benchmarks.
arXiv Detail & Related papers (2026-02-01T11:21:30Z) - Linguistic Structure from a Bottleneck on Sequential Information Processing [5.850665541267672]
We show that natural-language-like systematicity arises in codes that are constrained by predictive information.
We show that human languages are structured to have low predictive information at the levels of phonology, morphology, syntax, and semantics.
arXiv Detail & Related papers (2024-05-20T15:25:18Z) - From Word Models to World Models: Translating from Natural Language to
the Probabilistic Language of Thought [124.40905824051079]
We propose rational meaning construction, a computational framework for language-informed thinking.
We frame linguistic meaning as a context-sensitive mapping from natural language into a probabilistic language of thought.
We show that LLMs can generate context-sensitive translations that capture pragmatically-appropriate linguistic meanings.
We extend our framework to integrate cognitively-motivated symbolic modules.
arXiv Detail & Related papers (2023-06-22T05:14:00Z) - Variational Cross-Graph Reasoning and Adaptive Structured Semantics
Learning for Compositional Temporal Grounding [143.5927158318524]
Temporal grounding is the task of locating a specific segment from an untrimmed video according to a query sentence.
We introduce a new Compositional Temporal Grounding task and construct two new dataset splits.
We argue that the inherent structured semantics inside the videos and language is the crucial factor to achieve compositional generalization.
arXiv Detail & Related papers (2023-01-22T08:02:23Z) - O-Dang! The Ontology of Dangerous Speech Messages [53.15616413153125]
We present O-Dang!: The Ontology of Dangerous Speech Messages, a systematic and interoperable Knowledge Graph (KG)
O-Dang! is designed to gather and organize Italian datasets into a structured KG, according to the principles shared within the Linguistic Linked Open Data community.
It provides a model for encoding both gold standard and single-annotator labels in the KG.
arXiv Detail & Related papers (2022-07-13T11:50:05Z) - Compositional Generalization in Grounded Language Learning via Induced
Model Sparsity [81.38804205212425]
We consider simple language-conditioned navigation problems in a grid world environment with disentangled observations.
We design an agent that encourages sparse correlations between words in the instruction and attributes of objects, composing them together to find the goal.
Our agent maintains a high level of performance on goals containing novel combinations of properties even when learning from a handful of demonstrations.
arXiv Detail & Related papers (2022-07-06T08:46:27Z) - Neural Abstructions: Abstractions that Support Construction for Grounded
Language Learning [69.1137074774244]
Leveraging language interactions effectively requires addressing limitations in the two most common approaches to language grounding.
We introduce the idea of neural abstructions: a set of constraints on the inference procedure of a label-conditioned generative model.
We show that with this method a user population is able to build a semantic modification for an open-ended house task in Minecraft.
arXiv Detail & Related papers (2021-07-20T07:01:15Z) - Low-Dimensional Structure in the Space of Language Representations is
Reflected in Brain Responses [62.197912623223964]
We show a low-dimensional structure where language models and translation models smoothly interpolate between word embeddings, syntactic and semantic tasks, and future word embeddings.
We find that this representation embedding can predict how well each individual feature space maps to human brain responses to natural language stimuli recorded using fMRI.
This suggests that the embedding captures some part of the brain's natural language representation structure.
arXiv Detail & Related papers (2021-06-09T22:59:12Z) - A Benchmark for Systematic Generalization in Grounded Language
Understanding [61.432407738682635]
Humans easily interpret expressions that describe unfamiliar situations composed from familiar parts.
Modern neural networks, by contrast, struggle to interpret novel compositions.
We introduce a new benchmark, gSCAN, for evaluating compositional generalization in situated language understanding.
arXiv Detail & Related papers (2020-03-11T08:40:15Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.