The article compares Russian- and Uzbek-language clinical texts from the perspective of the semantic fields of “illness” and “treatment” and examines how AI models reconstruct symptom–diagnosis–therapy links. Based on a small corpus sample, three blocks are identified: symptomatology, nosological nominations, and therapeutic designations. Russian is characterized by stronger terminological standardization, which increases the accuracy of automatic linking. In Uzbek, difficulties arise from low-resource conditions, agglutinative morphology, the doublet nature of scientific and folk nominations, and the metaphorical character of descriptions. Typical AI errors are observed, including incorrect handling of negation and modality, confusion of polysemous units, and template-based overgeneralization. The article proposes recommendations to improve the reliability of bilingual medical NLP systems.
| Mualliflar | Turaev, Alisher, Тураев, Алишер, Turayev, Alisherjon |
|---|---|
| Jurnal | Хорижий лингвистика ва лингводидактика |
| Nashr sanasi | 2026-01-25 |
| Jild | 4 |
| Son | 1/S |
| Betlar | 155-159 |
| Til | O‘zbek |
| DOI | 10.47689/2181-3701-vol4-iss1/s-pp155-159 |
DOI: 10.47689/2181-3701-vol4-iss1/s-pp155-159 · Maqolaning asl sahifasi
семантическое поле, клинический текст, симптомы, диагноз, терапия, искусственный интеллект, русский язык, узбекский язык, semantic field, clinical text, symptom, diagnosis, therapy, terminological doublets, Russian, Uzbek, medical NLP, ontology, semantik maydon, klinik matn, simptom, diagnoz, terapiya, dublet termin, rus tili, o‘zbek tili, tibbiy NLP, ontologiya
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