| DC Field | Value | Language |
|---|
| dc.contributor.author | Litvinova, T. A. | - |
| dc.contributor.author | Ippolitov, Y. A. | - |
| dc.contributor.author | Seredin, P. V. | - |
| dc.date.accessioned | 2025-12-04T09:41:23Z | - |
| dc.date.available | 2025-12-04T09:41:23Z | - |
| dc.date.issued | 2025 | - |
| dc.identifier.citation | Litvinova, T.A. From STM to GPT: a comparative study of topic modeling methods for AI in dentistry / T.A. Litvinova, Y.A. Ippolitov, P.V. Seredin // Научный результат. Сер. Вопросы теоретической и прикладной лингвистики. - 2025. - Т.11, №3.-С. 85-121. - Doi: 10.18413/2313-8912-2025-11-3-0-5. - Библиогр.: с. 116-120. | ru |
| dc.identifier.uri | http://dspace.bsuedu.ru/handle/123456789/65961 | - |
| dc.description.abstract | This study presents a comprehensive topic modeling analysis of scientific abstracts in the field of artificial intelligence (AI) applied to dentistry. Three complementary approaches were compared: Structural Topic Modeling (STM); embedding-based clustering using the Leiden algorithm on OpenAI text embeddings; and zero-shot GPT-based topic modeling | ru |
| dc.language.iso | en | ru |
| dc.subject | linguistics | ru |
| dc.subject | computational linguistics | ru |
| dc.subject | topic modeling | ru |
| dc.subject | large language models | ru |
| dc.subject | dentistry | ru |
| dc.subject | scientometric analysis | ru |
| dc.subject | bibliometric methods | ru |
| dc.subject | artificial intelligence | ru |
| dc.title | From STM to GPT: a comparative study of topic modeling methods for AI in dentistry | ru |
| dc.type | Article | ru |
| Appears in Collections: | Т. 11, № 3
|
