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Machine learningDeep learning / NLP / CV

Pemodelan Topik Multilingual

Pemodelan topik multilingual memperluas model topik probabilistik seperti LDA kepada korpus yang merangkumi dua bahasa atau lebih, menyimpulkan topik laten yang dikongsi merentasi sempadan bahasa. Dengan mengikat taburan topik merentasi bahasa, ia membolehkan analisis dokumen rentas bahasa, penemuan topik yang boleh dibandingkan, dan dapatan maklumat tanpa memerlukan korpus selari penuh.

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Sumber

  1. Mimno, D., Wallach, H. M., Naradowsky, J., Smith, D. A., & McCallum, A. (2009). Polylingual topic models. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 880–889. ACL. link
  2. Vulić, I., De Smet, W., & Moens, M.-F. (2015). Monolingual and cross-lingual information retrieval models based on (bilingual) word embeddings. In Proceedings of SIGIR 2015, pp. 363–372. ACM. link

Cara memetik halaman ini

ScholarGate. (2026, June 3). Multilingual Topic Modeling (Cross-lingual Latent Topic Inference). ScholarGate. https://scholargate.app/ms/deep-learning/multilingual-topic-modeling

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ScholarGateMultilingual topic modeling (Multilingual Topic Modeling (Cross-lingual Latent Topic Inference)). Dicapai 2026-06-15 daripada https://scholargate.app/ms/deep-learning/multilingual-topic-modeling · Set data: https://doi.org/10.5281/zenodo.20539026