dzNLP at NADI 2024 Shared Task: Multi-Classifier Ensemble with Weighted Voting and TF-IDF Features
Year: 2024
Type: conference-paper
Abstract: This paper presents the contribution of our dzNLP team to the NADI 2024 shared task, specifically in Subtask 1 -Multi-label Countrylevel Dialect Identification (MLDID) (Closed Track).We explored various configurations to address the challenge: in Experiment 1, we utilized a union of n-gram analyzers (word, character, character with word boundaries) with different n-gram values; in Experiment 2, we combined a weighted union of Term Frequency-Inverse Document Frequency (TF-IDF) features with various weights; and in Experiment 3, we implemented a weighted major voting scheme using three classifiers: Linear Support Vector Classifier (LSVC), Random Forest (RF), and K-Nearest Neighbors (KNN).Our approach, despite its simplicity and reliance on traditional machine learning techniques, demonstrated competitive performance in terms of F1-score and precision.Notably, we achieved the highest precision score of 63.22% among the participating teams.However, our overall F1 score was approximately 21%, significantly impacted by a low recall rate of 12.87%.This indicates that while our models were highly precise, they struggled to recall a broad range of dialect labels, highlighting a critical area for improvement in handling diverse dialectal variations.
Institution: University of Algiers Benyoucef Benkhedda
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4402671434