A Local Explainability Technique for Graph Neural Topic Models
Abstract Topic modelling is a Natural Language Processing (NLP) technique that has gained popularity in the recent past. It identifies word co-occurrence patterns inside a document corpus to reveal hidden topics. Graph Neural Topic Model (GNTM) is a topic modelling technique that uses Graph Neural N...
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| Главные авторы: | , , , |
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| Формат: | Artigo |
| Язык: | Inglês |
| Опубликовано: |
Springer Nature
2024-01-01
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| Серии: | Human-Centric Intelligent Systems |
| Предметы: | |
| Online-ссылка: | https://doi.org/10.1007/s44230-023-00058-8 |
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