Romanian Syllabication Using Machine Learning

Dinu, Liviu P.; Niculae, Vlad; Şulea, Octavia-Maria

doi:10.1007/978-3-642-40585-3_57

Cited by 11 publications

(1 citation statement)

References 3 publications

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“…For example, Romanian has 7 base syllabification rules (DOOM 2005), but there are several exceptions. Some are more general, like the hiatus-diphthong ambiguities or the different surfacing of the letter 'i' as a vocalic or non-vocalic element (Dinu, Niculae, and Sulea 2013). Others are rather particular, like the ones generated by foreign or compound words.…”

Section: The Romanian Language and Romanian Lexical Datasetsmentioning

confidence: 99%

RoLEX: The development of an extended Romanian lexical dataset and its evaluation at predicting concurrent lexical information

et al. 2022

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In this article, we introduce an extended, freely available resource for the Romanian language, named RoLEX. The dataset was developed mainly for speech processing applications, yet its applicability extends beyond this domain. RoLEX includes over 330,000 curated entries with information regarding lemma, morphosyntactic description, syllabification, lexical stress and phonemic transcription. The process of selecting the list of word entries and semi-automatically annotating the complete lexical information associated with each of the entries is thoroughly described. The dataset’s inherent knowledge is then evaluated in a task of concurrent prediction of syllabification, lexical stress marking and phonemic transcription. The evaluation looked into several dataset design factors, such as the minimum viable number of entries for correct prediction, the optimisation of the minimum number of required entries through expert selection and the augmentation of the input with morphosyntactic information, as well as the influence of each task in the overall accuracy. The best results were obtained when the orthographic form of the entries was augmented with the complete morphosyntactic tags. A word error rate of 3.08% and a character error rate of 1.08% were obtained this way. We show that using a carefully selected subset of entries for training can result in a similar performance to the performance obtained by a larger set of randomly selected entries (twice as many). In terms of prediction complexity, the lexical stress marking posed most problems and accounts for around 60% of the errors in the predicted sequence.

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Section: The Romanian Language and Romanian Lexical Datasetsmentioning

confidence: 99%