Gaussian Process Optimization Automates Hyphenation Pattern Generation

Researchers have developed a method using Gaussian Process Bayesian optimization to automatically generate hyphenation patterns, a crucial component for text processing systems. This approach formulates the selection of patgen program parameter profiles as a black-box hyperparameter optimization problem. The optimization objective balances precision, measured by an F_{1/7}-score, with a trade-off between trie size and accuracy. Evaluations across 17 datasets in 14 languages demonstrated that the GP-optimized profiles significantly improve F_{1/7} scores and reduce trie size compared to manually tuned profiles, making pattern generation more reproducible and less reliant on expert tuning. AI

IMPACT Automates a complex text processing task, potentially improving efficiency and reproducibility in typesetting and rendering engines.

RANK_REASON The cluster contains an academic paper detailing a new methodology for optimizing hyphenation patterns using Gaussian Process Bayesian optimization. [lever_c_demoted from research: ic=1 ai=0.4]

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• arXiv
• Bayesian optimization
• Gaussian process
• Hugging Face

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