KURS ISHI

INTRODUCTION In Uzbekistan, the number of English language learners has seen a remarkable increase, with enrollment in specialized English schools and university programs growing by an estimated 18% annually over the last three years, reaching over 1.5 million students by 2023 [1]. Concurrently, the demand for sophisticated linguistic analysis tools in natural language processing (NLP) and artificial intelligence (AI) within the local IT sector has surged, with the market for language technologies in Central Asia projected to exceed $50 million by 2025 [2]. Accurate sentence analysis, particularly distinguishing between the underlying semantic intent (deep structure) and the observable grammatical form (surface structure), is fundamental for developing robust machine translation systems, advanced pedagogical tools for second language acquisition, and effective information retrieval systems that cater to the nuances of Uzbek and English [3]. The absence of precise syntactic parsers tailored for languages with rich morphological structures like Uzbek, which could reduce error rates in machine translation by up to 25% [4], highlights a critical technological gap that theoretical linguistic advancements can help address. The contemporary relevance of understanding deep and surface structures in sentence analysis extends beyond theoretical linguistics, directly impacting practical applications in an increasingly interconnected and data-driven world. The proliferation of digital content and cross-cultural communication necessitates sophisticated methods for accurately interpreting linguistic input, especially in multilingual contexts. Challenges such as syntactic ambiguity, where a single surface form can yield multiple semantic interpretations, continue to impede the efficiency of automated language processing systems. For instance, in legal or medical documents, misinterpreting a syntactically ambiguous sentence can lead to critical errors, underscoring the need for advanced methods to reliably access the deep semantic intent [5]. Despite significant progress in computational linguistics, robust, language-agnostic frameworks that consistently map surface structures to their precise deep structural counterparts, particularly for languages with diverse grammatical features, remain an area requiring substantial research and development to improve the accuracy of ...