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.

在该研究中，我们提出了一种综合的途径来对乌兹别克语进行音节化，其中包括基于规则的技术和机器学习算法。我们的研究结果表明，这两种方法都具有高度准确性，超过了 99%。这项研究为未来在乌兹别克语及其他相关低资源土耳其语言中关于音节化及相关领域的研究提供了宝贵的见解和建议。

乌兹别克音节提取工具的设计与实现

Design and Implementation of a Tool for Extracting Uzbek Syllables

The identification of syllables within phonetic sequences is known as
syllabification. This task is thought to play an important role in natural
language understanding, speech production, and the development of speech
recognition systems. The concept of the syllable is cross-linguistic, though
formal definitions are rarely agreed upon, even within a language. In response,
data-driven syllabification methods have been developed to learn from
syllabified examples. These methods often employ classical machine learning
sequence labeling models. In recent years, recurrence-based neural networks
have been shown to perform increasingly well for sequence labeling tasks such
as named entity recognition (NER), part of speech (POS) tagging, and chunking.
We present a novel approach to the syllabification problem which leverages
modern neural network techniques. Our network is constructed with long
short-term memory (LSTM) cells, a convolutional component, and a conditional
random field (CRF) output layer. Existing syllabification approaches are rarely
evaluated across multiple language families. To demonstrate cross-linguistic
generalizability, we show that the network is competitive with state of the art
systems in syllabifying English, Dutch, Italian, French, Manipuri, and Basque
datasets.

使用深度学习模型，我们提出了一个新的用于音节划分的方法，并在多个语系下表现出与当前最佳方法一致的竞争力。

基于神经序列标注的语言无关音节划分

Language-Agnostic Syllabification with Neural Sequence Labeling

Syllabification does not seem to improve word-level RNN language modeling
quality when compared to character-based segmentation. However, our best
syllable-aware language model, achieving performance comparable to the
competitive character-aware model, has 18%-33% fewer parameters and is trained
1.2-2.2 times faster.

比较音节划分和基于字符划分，在词级 RNN 语言建模的质量提高方面效果不明显。 然而，我们最好的音节感知语言模型表现出与竞争性基于字符模型相当的性能，参数少了 18％-33％，并且训练速度提高了 1.2-2.2 倍。

音节感知的神经语言模型：无法打败字符感知的模型

Syllable-aware Neural Language Models: A Failure to Beat Character-aware  Ones

Machine Transliteration has come out to be an emerging and a very important
research area in the field of machine translation. Transliteration basically
aims to preserve the phonological structure of words. Proper transliteration of
name entities plays a very significant role in improving the quality of machine
translation. In this paper we are doing machine transliteration for
English-Punjabi language pair using rule based approach. We have constructed
some rules for syllabification. Syllabification is the process to extract or
separate the syllable from the words. In this we are calculating the
probabilities for name entities (Proper names and location). For those words
which do not come under the category of name entities, separate probabilities
are being calculated by using relative frequency through a statistical machine
translation toolkit known as MOSES. Using these probabilities we are
transliterating our input text from English to Punjabi.

本文采用基于规则的方法对英 - 旁遮普语进行机器音译，通过计算名实体和非名实体的概率以及使用统计机器翻译工具包 MOSES 计算相对频率来提高机器翻译的质量，同时进行音节划分。