| DC Field | Value | Language |
|---|
| dc.contributor.author | Firsanova, V. I. | - |
| dc.date.accessioned | 2024-12-17T08:12:27Z | - |
| dc.date.available | 2024-12-17T08:12:27Z | - |
| dc.date.issued | 2024 | - |
| dc.identifier.citation | Firsanova, V.I. A graph-based approach to closed-domain natural language generation / V.I. Firsanova // Научный результат. Сер. Вопросы теоретической и прикладной лингвистики. - 2024. - Т.10, №3.-С. 135-167. - Doi: 10.18413/2313-8912-2024-10-3-0-7. - Библиогр.: с. 162-167. | ru |
| dc.identifier.uri | http://dspace.bsu.edu.ru/handle/123456789/64153 | - |
| dc.description.abstract | The paper introduces a novel NLP architecture, the Graph-Based Block-to-Block Generation (G3BG), which leverages state-of-the-art deep learning techniques, the power of attention mechanisms, distributional semantics, graph-based information retrieval, and decentralized networks | ru |
| dc.language.iso | en | ru |
| dc.subject | linguistics | ru |
| dc.subject | applied linguistics | ru |
| dc.subject | language generation | ru |
| dc.subject | language understanding | ru |
| dc.subject | generative artificial intelligence | ru |
| dc.subject | large language models | ru |
| dc.subject | decentralized networks | ru |
| dc.subject | data encoding | ru |
| dc.subject | distributional semantics | ru |
| dc.subject | closed-domain systems | ru |
| dc.title | A graph-based approach to closed-domain natural language generation | ru |
| dc.type | Article | ru |
| Appears in Collections: | Т. 10, вып. 3
|
