@inproceedings{wuraola-etal-2026-slang,
title = "{SLANG}-{G}raph{RAG}: Multi-Layered Retrieval with Domain-Specific Knowledge for Low Resource Social Media Conversations",
author = "Wuraola, Ifeoluwa and
Marciniak, Daniel and
Dethlefs, Nina",
editor = "Demberg, Vera and
Inui, Kentaro and
Marquez, Llu{\'i}s",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EACL} 2026",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-eacl.203/",
pages = "3919--3931",
ISBN = "979-8-89176-386-9",
abstract = "Emotion classification on social media is especially difficult when texts include informal, culturally grounded language like slang. Standard NLP benchmarks often miss these nuances, particularly in low-resource settings. We present SLANG-GraphRAG, a retrieval-augmented framework that integrates a culture-specific slang knowledge graph into large language models via one-shot prompting. Using multiple retrieval strategies, we incorporate slang definitions, regional usage, and conversational context. Our results show that incorporating structured cultural knowledge into the retrieval process leads to significant improvements, improving accuracy by up to 31{\%} and F1 score by 28{\%}, outperforming traditional and unstructured retrieval methods. To better evaluate model behavior, we propose a probabilistic metric that reflects the distribution of human annotations, providing a more nuanced measure of performance. This highlights the value of culturally sensitive applications and more balanced evaluation in subjective NLP tasks."
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<abstract>Emotion classification on social media is especially difficult when texts include informal, culturally grounded language like slang. Standard NLP benchmarks often miss these nuances, particularly in low-resource settings. We present SLANG-GraphRAG, a retrieval-augmented framework that integrates a culture-specific slang knowledge graph into large language models via one-shot prompting. Using multiple retrieval strategies, we incorporate slang definitions, regional usage, and conversational context. Our results show that incorporating structured cultural knowledge into the retrieval process leads to significant improvements, improving accuracy by up to 31% and F1 score by 28%, outperforming traditional and unstructured retrieval methods. To better evaluate model behavior, we propose a probabilistic metric that reflects the distribution of human annotations, providing a more nuanced measure of performance. This highlights the value of culturally sensitive applications and more balanced evaluation in subjective NLP tasks.</abstract>
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%0 Conference Proceedings
%T SLANG-GraphRAG: Multi-Layered Retrieval with Domain-Specific Knowledge for Low Resource Social Media Conversations
%A Wuraola, Ifeoluwa
%A Marciniak, Daniel
%A Dethlefs, Nina
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Findings of the Association for Computational Linguistics: EACL 2026
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-386-9
%F wuraola-etal-2026-slang
%X Emotion classification on social media is especially difficult when texts include informal, culturally grounded language like slang. Standard NLP benchmarks often miss these nuances, particularly in low-resource settings. We present SLANG-GraphRAG, a retrieval-augmented framework that integrates a culture-specific slang knowledge graph into large language models via one-shot prompting. Using multiple retrieval strategies, we incorporate slang definitions, regional usage, and conversational context. Our results show that incorporating structured cultural knowledge into the retrieval process leads to significant improvements, improving accuracy by up to 31% and F1 score by 28%, outperforming traditional and unstructured retrieval methods. To better evaluate model behavior, we propose a probabilistic metric that reflects the distribution of human annotations, providing a more nuanced measure of performance. This highlights the value of culturally sensitive applications and more balanced evaluation in subjective NLP tasks.
%U https://aclanthology.org/2026.findings-eacl.203/
%P 3919-3931
Markdown (Informal)
[SLANG-GraphRAG: Multi-Layered Retrieval with Domain-Specific Knowledge for Low Resource Social Media Conversations](https://aclanthology.org/2026.findings-eacl.203/) (Wuraola et al., Findings 2026)
ACL