方法证据记录
Domain-adaptive Doc2Vec
Domain-adaptive Doc2Vec adapts the Paragraph Vector (Doc2Vec) framework so that document embeddings learned on a source domain transfer effectively to a target domain. By aligning the representation space across domains during or after training, the model produces embeddings that are informative on both, enabling cross-domain classification, sentiment analysis, and retrieval with limited target-domain labels.
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
Domain-Adaptive Paragraph Vector (Doc2Vec) for Cross-Domain Document Representation
分类方法记录 · ml-model / deep-learning
- 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. · URL
- 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
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