This paper presents Multi_mil, a multilingual annotated corpus designed for the analysis of information operations in military discourse. The corpus consists of 1000 texts collected from social media and news platforms in Russian, Kazakh, and English, covering military and geopolitical narratives. A multi-level annotation scheme was developed, combining entity categories (e.g., military terms, geographical references, sources) with pragmatic features such as information operation type, emotional tone, author intent, and fake claim indicators. Annotation was performed manually in Label Studio with high inter-annotator agreement ($\mathsf{\kappa }$ = 0.82). To demonstrate practical applicability, baseline models and the proposed Onto-IO-BERT architecture were tested, achieving superior performance (macro-F1 = 0.81). The corpus enables the identification of manipulation strategies, rhetorical patterns, and cognitive influence in multilingual contexts. Multi_mil contributes to advancing NLP methods for detecting disinformation, propaganda, and psychological operations.

This paper presents a multi-level annotation model for detecting fake news in Kazakh and Russian languages, aiming to enhance understanding of disinformation strategies in multilingual digital media environments. Unlike traditional binary models, our approach captures the complexity of disinformation by accounting for both linguistic and cultural factors. To support this, a corpus of over 5000 news texts was manually annotated using the Label Studio platform. The annotation scheme consists of seven interrelated categories: CLAIM, SOURCE, EVIDENCE, DISINFORMATION_TECHNIQUE, AUTHOR_INTENT, TARGET_AUDIENCE, and TIMESTAMP. Inter-annotator agreement, evaluated using Cohen’s Kappa, ranged from 0.72 to 0.81, indicating substantial consistency. The annotated data reveals recurring patterns of disinformation, such as emotional manipulation, targeting of vulnerable individuals, and the strategic concealment of intent. Semantic relations between entities, such as CLAIM → EVIDENCE and CLAIM → AUTHOR_INTENT were formalized to represent disinformation narratives as knowledge graphs. This study contributes the first linguistically and culturally adapted annotation model for Kazakh and Russian languages, providing a robust and empirical resource for building interpretable and context-aware fake news detection systems. The resulting annotated corpus and its semantic structure offer valuable empirical material for further research in natural language processing, computational linguistics, and media studies in low-resource language environments.

This paper presents a comprehensive study aimed at systematically analyzing and evaluating natural language processing (NLP) techniques for military information operations, with a special focus on social media intelligence. Among an ever-growing complicated information environment, NLP methods like sentiment analysis, named entity recognition, and topic modeling have been essential in tracking online propaganda efforts, discovering emerging issues and threats globally with dialogues on military operations. These techniques make an impact on available decision making via situational awareness and getting the added extraction from volumes of unstructured data outputs thus increasing the overall strategic benefits to military organizations. There are technical and operational challenges concerning the use of NLP in a military context such as requirements for real-time data processing; language diversity; and maintaining data privacy while preserving ethical standards. To address these challenges, the study conducts an exhaustive survey of NLP methods, reviewing their range of applications, and highlights the relevance of several approaches for military information operations, with special emphasis on social media intelligence. The work further provides discussion on the comprehensive adoption of artificial intelligence (AI), edge computing, and multilingual NLP models for enhancing adaptability, efficiency, and transparency of the systems. It also extols the need for explainable AI (XAI) to improve accountability and trust by making term or even whole early warning systems derived from NLP analyses, transparent and interpretable for these military research applications with significant financial consequences. The paper also emphasizes the strategic importance of multilingual and multimodal analysis and the integration of specialized military lexicons to improve the contextual understanding of military discourse in social media environments. We also elucidate the important capabilities of NLP in enabling military operations to be responsive, rapid and data-driven while also adapting to the evolving nature of warfare. Key conclusions suggest that applying advanced NLP tools enhances situational awareness, enables timely threat detection, and supports more agile, data-informed decision-making within modern military operations. The paper shows a perspective to optimize NLP and AI technologies, leveraging various perspectives to benefit the operational needs of military and defense sectors in more data-rich environments.

Integrating structured linguistic resources into deep learning architectures represents a key challenge in domain-oriented NLP. This study proposes a framework for incorporating knowledge from a military thesaurus of the Ground Forces, structured according to the XML Zthes standard, into pre-trained transformed language models, including KazBERT, multilingual BERT, and XLM-RoBERTA. The approach addresses two interrelated tasks in specialized terminology processing: concept linking and semantic search. Unlike existing knowledge-injection methods designed primarily for general-domain applications, this framework formalizes the mapping of Zthes elements, such as Term, Broader term, Narrower term, Related term, ScopeNote, Language, and Source into structured textual representations that can be directly processed by transformer architectures. Fine-tuning is conducted on a dataset of 18,400 training instances automatically generated from the thesaurus, including synonym pairs, hierarchical relations (hyperonymy and hyponymy), associative links, and definitional descriptions. Experimental evaluation demonstrated that thesaurus-enriched models outperform baseline architectures across all major metrics. XLM-RoBERTA model achieves F1 = 0.84 and Top-5 accuracy = 0.94 in the concept linking task, representing a five-point improvement over the baseline. The model reaches Macro-F1 = 0.84 across four relation types. Results obtained on a specialized test set derived from terminology databases of Kazakhstan’s Armed Forces confirm robust cross-lingual generalization across Kazakh, Russian and English military discourse.

The article presents an annotated corpus consisting of clinical texts in Russian, obtained by exomic sequencing. This corpus was developed to support the tasks of automatically identifying named objects and semantic relationships in relation to genes, mutations, hereditary diseases, phenotypic traits and their clinical significance. During the formation of the corpus, reports of actual clinical exomic sequencing were used, the data went through the stages of preliminary anonymization and text normalization. The labeling process used international standards and knowledge bases such as HGVS, OMIM, ClinVar, and HPO, and ensured consistency and accuracy of biomedical information. The corpus contains more than 25,000 biomedical objects and more than 6,000 semantic links, making it an important resource in the field of clinical genetics in terms of volume and content. The annotation was carried out manually with the participation of several experts, and the results were compared by cross-checking, and the level of agreement between the annotators was assessed using special indicators. The results obtained indicate the high quality and reliability of the case. The finished corpus makes it possible to effectively use natural language processing models in the field of medical genetics for teaching and evaluation, development of clinical decision support systems, and applied research for structuring genetic data.

This article examines the problem of automatic implementation of new concepts in ontology in the context of constant updating of knowledge and increasing the volume of textual data. The object of research is the process by which new concepts can be integrated into hierarchical and non-taxonomic structures of ontology, while maintaining their logical and semantic consistency. The purpose of the work is to create a three-stage structure to automate the search, refinement, and optimal implementation of ideas. This structure should be built on the basis of modern machine learning and natural language processing technologies. Logical inference, contrast learning and large language models and pre-prepared language models are used as research methods. At the initial stage of the framework, larger language models are used to create formal OWL axioms and semantic connections, taking into account universal and existential logical constraints. In the second stage, contrast learning is used to refine vector representations of ideas and improve classification accuracy. The third stage is the logical verification and improvement of non-taxonomic relations with the help of ontological reasoners and external sources of knowledge. The biomedical ontology of SNOMED CT and the corpus of Kazakh data were used for experimental evaluation. The results show significant statistical superiority over existing methods, as well as high accuracy of concept placement (up to 91%). The main scientific value of the work lies in the fact that it integrates logical constraints and contextual analysis into the task of ontological expansion. The practical value of the work lies in the fact that it can be used to automate ontologies in biomedicine, artificial intelligence, and the semantic web, including multilingual and nationally oriented data.