Automatic syllabification in English: a comparison of different algorithms.
Automatic syllabification of words is challenging, not least because the syllable is not easy to define precisely. Consequently, no accepted standard algorithm for automatic syllabification exists. There are two broad approaches: rule-based and data-driven. The rule-based method effectively embodies...
| Publicado en: | Language & Speech Vol. 52; no. 1; pp. 1 - 28 |
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| Autores principales: | , , |
| Formato: | algorithm equations & formulas research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
Mar2009
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=105484691&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105484691 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00238309 3YY jtl: Language & Speech issn: 00238309 maglogo: Y pubinfo: dt: Mar2009 vid: 52 iid: 1 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 105484691 2010230833 10.1177/0023830908099881 105484691 ppf: 1 ppct: 27 formats: fmt: @attributes: type: P tig: atl: Automatic syllabification in English: a comparison of different algorithms. aug: au: Marchand Y Adsett CR Damper RI affil: Institute for Biodiagnostics (Atlantic), National Research Council Canada sug: subj: Algorithms Linguistics England Theory Comparative Studies Databases Empirical Research England Phonology Predictive Research Spelling Statistical Significance Subject Headings Validity Vocabulary Human ab: Automatic syllabification of words is challenging, not least because the syllable is not easy to define precisely. Consequently, no accepted standard algorithm for automatic syllabification exists. There are two broad approaches: rule-based and data-driven. The rule-based method effectively embodies some theoretical position regarding the syllable, whereas the datadriven paradigm tries to infer 'new' syllabifications from examples assumed to be correctly syllabified already. This article compares the performance of several variants of the two basic approaches. Given the problems of definition, it is difficult to determine a correct syllabification in all cases and so to establish the quality of the 'gold standard' corpus used either to evaluate quantitatively the output of an automatic algorithm or as the example-set on which data-driven methods crucially depend. Thus, we look for consensus in the entries in multiple lexical databases of pre-syllabified words. In this work, we have used two independent lexicons, and extracted from them the same 18,016 words with their corresponding (possibly different) syllabifications. We have also created a third lexicon corresponding to the 13,594 words that share the same syllabifications in these two sources. As well as two rule-based approaches (Hammond's and Fisher's implementation of Kahn's), three data-driven techniques are evaluated: a look-up procedure, an exemplar-based generalization technique, and syllabification by analogy (SbA). The results on the three databases show consistent and robust patterns. First, the data-driven techniques outperform the rule-based systems in word and juncture accuracies by a very significant margin but require training data and are slower. Second, syllabification in the pronunciation domain is easier than in the spelling domain. Finally, best results are consistently obtained with SbA. pubtype: Academic Journal doctype: algorithm equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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