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.

翻译：单词的准确音节划分在各种自然语言处理应用中起着至关重要的作用。音节划分是一种多功能语言学工具，广泛应用于语言学研究、语言技术、教育以及需要理解和处理语言的各个领域。本文提出了一种针对乌兹别克语音节划分的综合方法，包括基于规则的技术和机器学习算法。我们的基于规则方法采用先进技术将单词划分为音节、生成换行连字符并统计音节数量。此外，我们收集了一个用于机器学习算法评估与训练的数据集，包含单词-音节映射、连字符及音节计数，以预测音节数量并评估所提模型。实验结果表明，两种方法均能有效实现准确的音节划分。我们的实验结果显示，两种方法的准确率均超过99%。本研究为乌兹别克语本身以及其他资源匮乏的近亲突厥语系语言的音节划分及相关领域的未来研究提供了有价值的见解和建议。