Aug 2026· Международный Журнал Теоретических и Прикладных Вопросов Цифровых Технологий· 0 citations· 8 references
TL;DR
The section concludes with formal problem specification: given vocabulary V and corpus C, semantic analysis is formalized as a mapping problem preserving distributional properties, an optimization problem minimizing loss through gradient-based methods, and an evaluation problem assessing quality through semantic similarity, analogy, and downstream NLP task performance.
Abstract
Semantic analysis has become a central challenge in natural language processing, driven by exponential growth in digitized textual data and the need for automated content processing across multiple applications including machine translation, text classification, sentiment analysis, and information retrieval. However, while semantic analysis methods are well-developed for resource-rich languages such as English, morphologically complex languages like Uzbek suffer from deficiencies in annotated corpora, lexical-semantic resources, and high-quality vector models – a gap amplified by governmental initiatives in digital economy development and national language technology advancement. This section grounds semantic analysis in the distributional semantics hypothesis principle that words exhibiting similar contexts possess similar meanings – thereby recasting the problem as a geometric challenge within continuous vector spaces. Two principal mathematical strategies are formalized: (1) prediction-based models (word2vec: CBOW/Skip-gram), which optimize context prediction objectives, and (2) count-based models (GloVe), which leverage global co-occurrence statistics through matrix factorization. Both project high-dimensional word co-occurrence relationships into low-dimensional dense vector spaces, enabling semantic analogy representation. For resource-scarce languages like Uzbek, cross-lingual embedding alignment (Procrustes optimization) enables semantic knowledge transfer from resource-rich languages, facilitating shared semantic spaces across the Turkic language family. The section concludes with formal problem specification: given vocabulary V and corpus C, semantic analysis is formalized as (1) a mapping problem preserving distributional properties, (2) an optimization problem minimizing loss through gradient-based methods, and (3) an evaluation problem assessing quality through semantic similarity, analogy, and downstream NLP task performance.
Automatic Text Recognition (ATR) now supplies digital humanities with large volumes of unstructured, heterogeneous, and often noisy text in ancient languages. Downstream semantic analysestext reuse identification, alignment, and semantic search-rely on sentence embeddings, yet existing methods transfer poorly to ancient languages: generic multilingual encoders underperform, specialized language models yield anisotropic representation spaces, and labeled similarity data is unavailable. We study two fully unsupervised strategies - TSDAE and contrastive sentence embedding (CSE) - that adapt a specialized token-level language model into a corpus-specific sentence encoder using only raw sentences. On the philologically central case of biblical reuse in patristic literature (2,935 expert-verified parallels in Latin and Ancient Greek, from Augustine, Jerome, and Athanasius), we decompose reuse identification into two separately evaluated tasks-binary detection and correspondence retrieval-and benchmark the adapted encoders against multilingual, specialized, distilled, and supervised fine-tuned baselines, as well as on artificially noised data simulating HTR artifacts and scribal abbreviations. The adapted encoders outperform all baselines on both tasks, with complementary profiles: TSDAE leads detection given a large in-domain corpus, while CSE leads retrieval, reaches its optimum with as few as 4-8k raw in-domain sentences-a few tens of seconds of training on a laptop GPU-and transfers across works and authors, including to noisy post-ATR text when retrained directly on it. UMAP atlases relate the geometric effect of each strategy to the measured gains, and the full pipeline-segmentation, fine-tuning, cross-corpus semantic search-is made available to non-specialists through the online tool Paraphrasis.
A comprehensive review of the evolution of NLP from traditional rule-based approaches to modern transformer models including BERT and GPT demonstrates that NLP continues to transform intelligent systems and is expected to play an increasingly significant role in the development of next-generation AI technologies.
P. Kalaiselvi· International Journal of Eme...· 0 citations
This study highlights the importance of integrating in-depth linguistic analysis, contextual semantic modelling, and cultural awareness into natural language processing-based translation systems by combining traditional linguistic insights with computational methods.
Hilal Abdul-Raziq Sadiq, Zaxid Maxmudovich Islamov, R. Matibaeva et al.· Digital Technologies Researc...· 0 citations
This study validates the effectiveness of the BERT model in semantic similarity calculation, providing more accurate technical support for related application scenarios, and laying the foundation for subsequent model optimization and lightweighting research.
Jia-Cheng Gao· International Conference on...· 0 citations
This paper revisits semantic projections and related count-based representations as interpretable directional semantic structures for semantic analysis in document corpora and web-based information environments and demonstrates that semantic projections effectively capture persistent contextual structures while remaining sensitive to corpus-specific discourse communities.
Mabel López-Bordao, Antonia Ferrer-Sapena, Pablo Lara-Navarra et al.· Information· 0 citations
Event detection from text streams is a fundamental problem in natural language processing, with applications ranging from social monitoring to information retrieval. In Persian, the task is further complicated by the scarcity of labeled resources, the informal and highly dynamic nature of user-generated content, and the rich morphological and orthographic diversity of the language. Most existing approaches represent events as collections of isolated words, discarding the compositional semantic structure through which real-world events—particularly emerging ones—are most naturally expressed. We propose an unsupervised, phrase-based framework for event detection from Persian social media streams, in which semantically meaningful phrases serve as the fundamental units of event representation. Text segmentation is reformulated as an optimization problem, and the central contribution of this work is the definition of its objective function: rather than relying on traditional statistical resources, pre-trained language models are employed directly as the scoring backbone of the optimization process. The framework is instantiated and compared under three configurations—a statistical n-gram model built from Wikipedia, the multilingual model mBERT, and the Persian-specific model ParsBERT—enabling a controlled analysis of how the nature and capacity of the scoring function affect event detection quality. Experiments on a Persian Telegram dataset demonstrate that, under a unified evaluation protocol applied identically to all methods, the proposed framework outperforms word-based approaches and established baselines on both clustering coherence and topic recall, although no single parameter configuration of the proposed method maximizes both metrics simultaneously, and the reported gains are therefore metric-dependent. The language model–based configurations, in particular, show superior ability to identify novel and previously unseen phrases, owing to their capacity to evaluate semantic coherence independently of surface frequency. Beyond event detection, the principle of employing language model output distributions as formal objective functions within optimization algorithms represents a generalizable design paradigm with potential applicability to a broader class of natural language processing tasks.
Pejman Gholami-Dastgerdi, M. Feizi-Derakhshi, P. Salehpour· IEEE Access· 0 citations
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