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Machine learningDeep learning / NLP / CV

Multimodal Word2Vec

Multimodal Word2Vec udvider det klassiske Word2Vec-framework ved at forankre ordrepræsentationer i perceptuelle signaler – typisk billedtræk – sideløbende med distributionelle tekststatistikker. Resultatet er ordvektorer, der indfanger både lingvistiske co-occurrence-mønstre og visuel mening, hvilket muliggør rigere vurderinger af semantisk lighed og bedre ydeevne på konceptniveauopgaver, hvor rent tekstbaserede indlejringer kommer til kort.

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Kilder

  1. Bruni, E., Tran, N.-K., & Baroni, M. (2014). Multimodal Distributional Semantics. Journal of Artificial Intelligence Research, 49, 1–47. DOI: 10.1613/jair.4135
  2. Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013). Distributed Representations of Words and Phrases and their Compositionality. Advances in Neural Information Processing Systems (NIPS), 26. link

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ScholarGate. (2026, June 3). Multimodal Word2Vec (Cross-Modal Distributional Semantics). ScholarGate. https://scholargate.app/da/deep-learning/multimodal-word2vec

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ScholarGateMultimodal Word2Vec (Multimodal Word2Vec (Cross-Modal Distributional Semantics)). Hentet 2026-06-15 fra https://scholargate.app/da/deep-learning/multimodal-word2vec · Datasæt: https://doi.org/10.5281/zenodo.20539026