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All Journal Bulletin of Electrical Engineering and Informatics
Adil Enaanai
Abdelmalek Essaâdi University
Electrical & Electronics Engineering

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Dynamic alpha factor optimization for hybrid semantic similarity in french word sense disambiguation Btissam El Janati; Adil Enaanai; Fadoua Ghanimi
Bulletin of Electrical Engineering and Informatics Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i4.11680

Abstract

Word sense disambiguation (WSD) remains critical for French, where polysemy complicates semantic interpretation. Hybrid approaches combining lexical and semantic methods typically rely on static weighting parameters that fail to adapt to varying contexts. A hybrid architecture integrating fuzzy Jaccard similarity with sentence-bidirectional encoder representations from transformers (SBERT) embeddings is proposed. A machine learning-based dynamic weighting mechanism replaces the fixed alpha=0.7. A Ridge regression model predicts the optimal alpha based on five features: entropy, Jaccard-SBERT disagreement, gloss length, context richness, and SBERT confidence. The model was trained on 20 sentences and validated on 13 sentences from a dataset of 33 ambiguous French phrases. Dynamic alpha achieves a 7.6% reduction in mean absolute error (MAE) (0.2397 to 0.2216) and a 7.3% reduction in root mean square error (RMSE) (0.2475 to 0.2294) compared to fixed alpha=0.7. Per-sentence gains reach 10.0%. Statistical analysis confirms significance (Wilcoxon, p=0.00195) with a small to medium effect size (Cohen's d=0.300). Feature coefficients reveal that disagreement (+0.41) and entropy (+0.32) are the most influential predictors.
Co-Authors Btissam El Janati Fadoua Ghanimi