GLTM: A Global and Local Word Embedding-Based Topic Model for Short Texts
Short texts have become a kind of prevalent source of information, and discovering topical information from short text collections is valuable for many applications. Due to the length limitation, conventional topic models based on document-level word co-occurrence information often fail to distill s...
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| Principais autores: | , , , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
IEEE
2018-01-01
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| coleção: | IEEE Access |
| Assuntos: | |
| Acesso em linha: | https://ieeexplore.ieee.org/document/8425711/ |
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