LINGUISTIC FEATURES OF AI-GENERATED TEXTS AND METHODS FOR THEIR AUTOMATED IDENTIFICATION

Texts Generated by Artificial Intelligence: Structure and Semantics

It was concluded that texts generated by artificial intelligence constitute a separate linguistic phenomenon with its own set of characteristics, which requires a special typology and a flexible, updatable analysis methodology.

Explainable AI Framework for Cognitive and Pragmatic Analysis of Classical Arabic Narratives Using Large Language Models

The results demonstrate that linear discriminative models and appropriate lexical feature engineering can provide a very accurate and interpretable baseline for the development of natural language processing algorithms for Arabic.

Characterization and Mechanisms of Lexical Complexity in AI-Generated Texts: A Comparative Corpus-Based Study

Analysis of AIGC texts points out that the complexity of AI text primarily stems from its mechanism of selecting vocabulary based on probability distributions, which favors longer words, abstract nouns, and words with high semantic content, thereby forming a highly compact linguistic surface.

Semantic analysis of problems in natural language processing and their mathematical interpretation

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.

Experimental Evaluation of the Reliability of AI-Generated Text Identification in Scientific Literature

Linguistic Features of AI-Generated Academic Texts and the Role of Human Editing

A statistically significant increase in lexical density and frequency of cohesive markers has been revealed, indicating an increase in information compression and explicit discursive organization of texts, consistent with characteristics described in AI-assisted writing studies.