Research of Means of Confirmation and Denial

Shodmonaliyeva Yulduz  Akramjon kizi

doi:10.51699/cajlpc.v6i4.1304

Shodmonaliyeva Yulduz Akramjon kizi Tashkent state university of uzbek language and literature named after Alisher Navoi, basic doctoral student

DOI: https://doi.org/10.51699/cajlpc.v6i4.1304

Keywords: Affirmative Category, Negative Category, UGM, Logic, NLP, Machine Learning, Judgment, Negative Verb Form, Zero Form, Negative Particle, Modalities, Word-Sentence

Abstract

Affirmation and negation are fundamental linguistic categories that reflect essential features of reality, serving as core concepts in philosophy, logic, and linguistics. Their accurate identification and application play a crucial role in communication, shaping meaning at both theoretical and applied levels. In the Uzbek language, these categories manifest through various verbal and nonverbal means, including morphological affixes, lexical forms, modal particles, gestures, emojis, and intonation, influencing sentence structure and pragmatic interpretation. While affirmation and negation have been extensively studied in Russian and English linguistics, comprehensive research on their full range of forms and pragmatic functions in Uzbek remains limited. This study aims to classify and analyze the diverse forms of expressing affirmation and negation in Uzbek, considering both linguistic and extralinguistic means, and to assess their role in determining the reliability and polarity of judgments. The analysis identified specific verbal indicators such as modal words, negative pronouns, and affixes, as well as nonverbal cues like gestures and emojis, which collectively convey affirmative or negative meanings. Comparative insights from Russian linguistic traditions, including Peshkovsky’s classification and RG-80 typology, were adapted to the Uzbek context. The integration of nonverbal communicative means with traditional morphological and lexical forms offers a more holistic framework for understanding affirmation and negation in Uzbek. These findings enhance theoretical models in linguistics and have practical applications in fields such as NLP, machine learning, and language teaching, where accurate polarity detection is essential for semantic processing and effective communication.

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