| Revista: | Computación y sistemas |
| Base de datos: | |
| Número de sistema: | 000560732 |
| ISSN: | 1405-5546 |
| Autores: | Ozaki, Kana1 Kobayashie, Ichiro1 |
| Instituciones: | 1Ochanomizu University, Kanto. Japón |
| Año: | 2022 |
| Periodo: | Jul-Sep |
| Volumen: | 26 |
| Número: | 3 |
| Paginación: | 1225-1232 |
| País: | México |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Resumen en inglés | Probabilistic topic models based on Latent Dirichlet Allocation (LDA) is widely used to extract latent topics from document collections. In recent years, a number of extended topic models have been proposed, especially Gaussian LDA (G-LDA) has attracted a lot of attention. G-LDA integrates topic modeling with word embeddings by replacing discrete topic distributions over words with multivariate Gaussian distributions on the word embedding space. This can reflect semantic information into topics. In this paper, we use G-LDA for our base topic model and apply Stochastic Variational Inference (SVI), an efficient inference algorithm, to estimate topics. Through experiments, we could extract the topics with high coherence in practical time. |
| Disciplinas: | Ciencias de la computación, Ciencias de la computación |
| Palabras clave: | Procesamiento de datos, Inteligencia artificial |
| Keyword: | Data processing, Artificial intelligence |
| Texto completo: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
