Augmented-syllabification of n-gram tagger for Indonesian words and named-entities

As one of the statistical-based models, an n-gram syllabification commonly gives a high syllable error rate (SER) for Bahasa Indonesia, one of the low-resource languages, since it fails for a high out-of-vocabulary (OOV) rate. Two previous models: bigram-syllabification with flipping onsets (BFO) an...

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Detalles Bibliográficos
Autores principales: Suyanto, Suyanto, Sunyoto, Andi, Ismail, Rezza Nafi, Romadhony, Ade, Sthevanie, Febryanti
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9708824/
https://www.ncbi.nlm.nih.gov/pubmed/36468140
http://dx.doi.org/10.1016/j.heliyon.2022.e11922

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9708824/
https://www.ncbi.nlm.nih.gov/pubmed/36468140
http://dx.doi.org/10.1016/j.heliyon.2022.e11922

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