dataset_info:
features:
- name: query_id
dtype: int64
- name: rel_score
dtype: int64
- name: doc_id
dtype: int64
- name: query
dtype: string
- name: document
dtype: string
splits:
- name: train
num_bytes: 325495
num_examples: 999
download_size: 108552
dataset_size: 325495
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
🎲 LudoSym: Gambling Addiction IR Dataset (Spanish)
Overview
This dataset accompanies the paper:
Analyzing Gambling Addictions: A Spanish Corpus for Understanding Pathological Behavior
Manuel Couto-Pintos, Marcos Fernández-Pichel, Mario Ezra Aragón, David E. Losada
Findings of the Association for Computational Linguistics: EMNLP 2025
The dataset is designed to support information retrieval and ranking research focused on the detection and analysis of gambling addiction symptoms from Spanish text. It enables the evaluation and training of retrieval models that associate user-defined symptom queries with relevant textual evidence.
Task Description
The dataset follows a standard IR / ranking formulation:
- Queries represent standardized gambling addiction symptoms.
- Documents are short Spanish text passages related to gambling behavior.
- Relevance judgments (qrels) indicate how relevant each document is for a given symptom query.
The task is to rank documents by relevance for each query, enabling:
- neural ranking
- symptom screening
- cross-encoder and bi-encoder training
- IR evaluation (e.g., nDCG, MAP, Recall)
Dataset Structure
Each example in the dataset has the following fields:
| Field | Type | Description |
|---|---|---|
query_id |
int | Unique identifier of the symptom query |
doc_id |
int | Unique identifier of the document |
query |
string | Text of the gambling addiction symptom |
document |
string | Spanish text passage |
relevance |
float | Relevance score between the query and the document |
Citation
If you use this resource, please cite:
@inproceedings{couto-etal-2025,
title = "Analyzing Gambling Addictions: A Spanish Corpus for Understanding Pathological Behavior",
author = "Couto-Pintos, Manuel and
Fernández-Pichel, Marcos and
Aragón, Mario Ezra and
Losada, David E.",
booktitle = "Findings of the 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP)"
}