Machine learningDeep learning / NLP / CV
域自适应Doc2Vec
域自适应Doc2Vec（Domain-adaptive Doc2Vec）对段落向量（Doc2Vec）框架进行了调整，使得在源域上学习到的文档嵌入能够有效地迁移到目标域。通过在训练期间或之后对齐跨域的表示空间，该模型生成的嵌入在两个域上都具有信息量，从而能够进行跨域分类、情感分析和检索，且目标域标签有限。
阅读完整方法
仅限会员
登录使用免费账户登录即可阅读本节。
Method map
The neighbourhood of related methods — select a node to explore.
来源
- Le, Q. V., & Mikolov, T. (2014). Distributed representations of sentences and documents. Proceedings of the 31st International Conference on Machine Learning (ICML 2014), PMLR 32(2), 1188–1196. link ↗
- Blitzer, J., McDonald, R., & Pereira, F. (2006). Domain adaptation with structural correspondence learning. Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing (EMNLP 2006), 120–128. DOI: 10.3115/1610075.1610094 ↗
如何引用本页
ScholarGate. (2026, June 3). Domain-Adaptive Paragraph Vector (Doc2Vec) for Cross-Domain Document Representation. ScholarGate. https://scholargate.app/zh/deep-learning/domain-adaptive-doc2vec
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- Doc2Vec文本挖掘↔ compare
- 基于领域自适应BERT的分类深度学习↔ compare
- 领域自适应句子嵌入 (Domain-Adaptive Sentence Embeddings)深度学习↔ compare
- 领域自适应 Word2Vec深度学习↔ compare
- 微调Doc2Vec深度学习↔ compare