Syllabification rules versus data-driven methods in a language with low syllabic complexity : the case of Italian

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DOIResolve DOI: http://doi.org/10.1016/j.csl.2009.02.004
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TypeArticle
Journal titleComputer Speech & Language
Volume23
Issue4
Pages444463; # of pages: 20
SubjectSyllabification; Italian language; Rule-based systems; Data-driven methods; Analogy
AbstractLinguistic 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 outperform 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 – Italian. 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 results show that data-driven methods can also outperform rule-based methods on Italian syllabification, a language of low syllabic complexity.
Publication date
LanguageEnglish
AffiliationNRC Institute for Biodiagnostics; National Research Council Canada
Peer reviewedYes
NPARC number20154420
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Record identifier1ccf3a5a-cc84-460b-91e6-7d886d72d9f4
Record created2012-06-22
Record modified2016-05-09
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