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Кластеризация лексики для решения задачи диагностики афазии
С. 276–277.
The article presents a study on the automatic clustering of lexicon for diagnosing aphasia in the Russian language. Using verbal fluency test data from 162 participants (both healthy individuals and those with aphasia), the authors applied an algorithm based on FastText distributional-semantic vectors and a clustering model using cosine similarity. The results revealed statistically significant differences between the groups in clustering metrics (t-score, Silhouette score) and lexical diversity, confirming the feasibility of creating an automated diagnostic model for Russian speech-language pathologists and special educators.
Language:
Russian
In book
М.: Институт языкознания РАН, 2025.