Design and Implementation of a Tool for Extracting Uzbek Syllables
Ulugbek Salaev Elmurod Kuriyozov Gayrat Matlatipov
The accurate syllabification of words plays a vital role in various Natural
Language Processing applications. Syllabification is a versatile linguistic
tool with applications in linguistic research, language technology, education,
and various fields where understanding and processing language is essential. In
this paper, we present a comprehensive approach to syllabification for the
Uzbek language, including rule-based techniques and machine learning
algorithms. Our rule-based approach utilizes advanced methods for dividing
words into syllables, generating hyphenations for line breaks and count of
syllables. Additionally, we collected a dataset for evaluating and training
using machine learning algorithms comprising word-syllable mappings,
hyphenations, and syllable counts to predict syllable counts as well as for the
evaluation of the proposed model. Our results demonstrate the effectiveness and
efficiency of both approaches in achieving accurate syllabification. The
results of our experiments show that both approaches achieved a high level of
accuracy, exceeding 99%. This study provides valuable insights and
recommendations for future research on syllabification and related areas in not
only the Uzbek language itself, but also in other closely-related Turkic
languages with low-resource factor.