CultureScope-XGBoost

WikiCulturalClassifier

Task Overview

The goal is to classify Wikipedia/Wikidata entities according to their cultural specificity. Each item belongs to one of three classes:

• CA – Cultural Agnostic: globally known with no identifiable cultural origin
• CR – Cultural Representative: originates from a specific culture but widely recognized
• CE – Cultural Exclusive: strongly linked to a single culture and limited global diffusion

The dataset used for training and evaluation is the following: Cultural Dataset

Dataset Usage

Key dataset facts:

• Source: Wikidata entity metadata and descriptions
• ~6k silver-annotated training examples + ~300 gold-annotated validation examples
• Contains entity URL, name, textual description, type, and category
• Culturality labels: CA, CR, CE

Project-specific split strategy:

• The provided training set was split into:

  • 80% for training
  • 20% for internal validation
  • Stratified by label to preserve distribution
  • Code reference: train_classifier.py

• The official validation set (gold labels) was used as the final evaluation set, treated as test data

This ensures no leakage from gold labels during model development.

---

Method Overview

This classifier does not use any large language models. Culturality is inferred through structured statistics from Wikipedia and Wikidata.

Feature engineering pipeline:

• Wikipedia statistics:

  • number of language editions
  • English page length, number of categories, external links

• Wikidata claims analysis:

  • cultural/geographic properties
  • identifier count and diversity

• Composite and interaction features:

  • cultural specificity score
  • global reach score
  • polynomial transformations and binary thresholds

Feature extraction and preprocessing code: extract_features.py, engineer_features() in train_classifier.py

Classifier and training:

• Model: XGBoost with GPU inference
• Objective: Multiclass soft probabilities (3 classes)
• Standardization applied to all continuous features
• Hyperparameters optimized with Optuna Code reference: tune_hyperparameters.py

The model artifact (best_model.pkl) includes:

• Trained XGBoost model
• Feature scaler
• Label encoder
• Feature list

---

Performance Summary

All metrics evaluated on the gold validation set:

Class-wise summary (high level):

• CA: high recall, strong detection of globally-known items
• CE: balanced performance
• CR: most challenging class due to ambiguity between global reach and origin