Machine learningDeep learning / NLP / CV
自监督Word2Vec
Word2Vec是Mikolov等人于2013年提出的一种浅层神经网络模型，它通过自监督目标，从大规模未标注文本语料库中学习词语的密集向量表示。通过训练模型来预测周围的上下文词（Skip-gram）或从上下文中预测目标词（CBOW），它在连续向量空间中捕捉到丰富的语义和句法规律，而无需任何人工标注。
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来源
- Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. In Proceedings of the International Conference on Learning Representations (ICLR 2013). link ↗
- Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems (NeurIPS 2013), 26. link ↗
如何引用本页
ScholarGate. (2026, June 3). Self-supervised Word2Vec (Skip-gram and CBOW with Self-supervised Objectives). ScholarGate. https://scholargate.app/zh/deep-learning/self-supervised-word2vec
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