Challenges of Syntactic Markup in The Uzbek Language

Аннотация

This article explores the key challenges related to syntactic markup in the Uzbek language. As an agglutinative language with free word order, Uzbek presents significant difficulties for natural language processing (NLP). Particular attention is paid to morphological ambiguity, the lack of large-scale annotated corpora, and the insufficient adaptation of algorithms to the linguistic specificities of Uzbek. Solutions are proposed, including the development of specialized corpora, adaptation of existing machine learning models, and the creation of new markup algorithms tailored to the language. Keywords: Syntactic markup, Uzbek language, morphology, agglutination, corpus, NLP, machine learning. Introduction: Syntactic markup is one of the core tasks According to Ozcelik and Gungor a comparative in natural language processing. For many widely analysis of syntactic markup methodologies for spoken languages, such as English and Chinese, high- agglutinative languages such as Turkish, offering accuracy syntactic markup systems have been insights that are highly relevant to the Uzbek language developed based on richly annotated corpora and context [6;98]. In parallel, Tursunov investigated the powerful machine learning models. However, for the distinct morphological and syntactic characteristics of Uzbek language, despite its broad usage, similar tools Uzbek that must be addressed in the development of remain underdeveloped. This situation is due to both effective NLP tools [4;114]. Collectively, this body of the linguistic structure of the language and the lack of work underscores both the opportunities and the computational resources. limitations of applying existing syntactic markup frameworks to Uzbek, emphasizing the necessity of Literature Review creating language-specific resources and tailored Numerous studies have explored syntactic annotation algorithms. in low-resource and agglutinative languages. Manning's METHODS development of the Stanford CoreNLP toolkit marked a significant advancement in syntactic analysis through This study is based on an analysis of existing rule-based and statistical approaches. Nevertheless, publications, available linguistic resources, and a the application of such models to the Uzbek language comparative approach with syntactic markup methods remains constrained due to the scarcity of annotated used for other agglutinative languages. The research training data. evaluates both rule-based and statistical/AI-driven approaches, including syntactic frameworks such as Devlin introduced BERT, a model that facilitates Universal Dependencies and neural architectures like contextual understanding across multiple languages. BERT and Transformer models. However, as noted by Nasriddinov and Abdullaeva, multilingual models such as mBERT tend to Expanded Results underperform when applied to morphologically rich To provide a comprehensive understanding of syntactic languages like Uzbek [5;76]. The Universal markup challenges in Uzbek, we extend the analysis to Dependencies project has contributed significantly to multiple linguistic dimensions. the standardization of syntactic annotations across 1. Morphological Complexity: Uzbek is an languages, including initial efforts aimed at developing agglutinative language where a single root can resources for Uzbek. American Journal Of Philological Sciences 29 https://theusajournals.com/index.php/ajps American Journal Of Philological Sciences (ISSN – 2771-2273) generate dozens of word forms via suffixation. This DISCUSSION makes tokenization, part-of-speech tagging, and The challenges described above highlight a systemic syntactic labeling interdependent and error-prone. For gap in resources and methodologies that must be example, the word “kelganimizdan” (from when we bridged to achieve effective syntactic markup for came) involves multiple grammatical layers that must Uzbek. While similar challenges have been be resolved before markup. encountered in other agglutinative and low-resource 2. Free Word Order: Uzbek allows permutations languages, Uzbek presents a unique case due to its of subject, object, and verb. Although SOV (Subject- sociolinguistic and orthographic diversity. As such, Object-Verb) is the neutral structure, practical usage cross-linguistic transfer techniques from Turkish or permits variations that defy rigid parsing models. This Finnish, though potentially helpful, are insufficient on freedom complicates dependency parsing and their own and must be adapted with care[3;79]. syntactic role assignment, especially in models trained One major avenue for improvement lies in community- on fixed-word-order languages. driven annotation efforts. Engaging native Uzbek 3. Ellipsis and Implicit Subjects: Pronouns and speakers, particularly from different dialect groups, in even subjects are often omitted. For instance, the development of annotated corpora can ensure “Bordim” translates as “(I) went”, where the subject is broader linguistic co