方法对比

	Doc2Vec	TF-IDF
领域	文本挖掘	文本挖掘
方法族	Process / pipeline	Process / pipeline
起源年份	2014	1988
提出者	Quoc V. Le & Tomas Mikolov	Salton & Buckley
类型	Document-embedding representation learning	Text vectorization / term-weighting scheme
开创性文献	Le, Q. V. & Mikolov, T. (2014). Distributed Representations of Sentences and Documents. Proceedings of the 31st International Conference on Machine Learning (ICML), 1188-1196.	Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523.
别名	paragraph vector, document embeddings, Doc2Vec Belge Gömülmeleri	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
相关	4	3
摘要	Doc2Vec, also known as Paragraph Vector, is a representation-learning method introduced by Le and Mikolov (2014) that maps whole documents to fixed-length dense vectors. These vectors place similar documents close together in space, supporting document comparison and classification.	TF-IDF, introduced by Salton and Buckley (1988), is a term-weighting scheme that scores each word in a document by how often it appears there and how rare it is across the whole collection. It turns raw text into weighted document vectors, giving high weight to terms that are frequent in one document but uncommon elsewhere.