Syllabification rules versus data-driven . . .

Linguistic rules have been assumed to be the best technique for determining the syllabification of unknown words. This has recently been challenged for the English language where data-driven algorithms have been shown to out-perform rule-based methods. It may be possible, however, that data-driven methods are only better for languages with complex syllable structures. In this study, three rule-based automatic syllabification systems and two data-driven automatic syllabification systems (Syllabification by Analogy and the Look-Up Procedure) are compared on a language with lower syllabic complexity — Ital-ian. Comparing the performance using a lexicon containing 44,720 words, the best data-driven algorithm (Syllabification by Analogy) achieved 97.70 % word accuracy while the best rule set correctly syllabified 89.77 % words. These re-sults show that data-driven methods can also outperform rule-based methods on Italian syllabification, a language of low syllabic complexity