השוואת שיטות

	Doc2Vec מונחה-למחצה	Doc2Vec	אלגוריתם התפשטות התוויות (Label Propagation)	Word2Vec
תחום	למידה עמוקה	כריית טקסט	למידת מכונה	כריית טקסט
משפחה	Machine learning	Process / pipeline	Machine learning	Process / pipeline
שנת המקור	2014–2017	2014	2002	2013
הוגה השיטה	Le, Q. V. & Mikolov, T. (base Doc2Vec); semi-supervised extensions by various authors circa 2015–2019	Quoc V. Le & Tomas Mikolov	Zhu, X. & Ghahramani, Z.	Tomas Mikolov et al.
סוג	Semi-supervised representation learning	Document-embedding representation learning	Graph-based semi-supervised classification	Neural word-embedding model
מקור מכונן	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.	Le, Q. V. & Mikolov, T. (2014). Distributed Representations of Sentences and Documents. Proceedings of the 31st International Conference on Machine Learning (ICML), 1188-1196.	Zhu, X., & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University.	Mikolov, T., Chen, K., Corrado, G. & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space.
כינויים	Semi-supervised Paragraph Vector, SS-Doc2Vec, Label-guided PV-DBOW, Semi-supervised PV-DM	paragraph vector, document embeddings, Doc2Vec Belge Gömülmeleri	LP, label spreading, graph-based semi-supervised learning, harmonic label propagation	word embeddings, skip-gram, continuous bag-of-words, Word2Vec Kelime Gömülmeleri
קשורות	3	4	3	4
תקציר	Semi-supervised Doc2Vec extends the Paragraph Vector framework of Le and Mikolov (2014) by training dense document embeddings on both labeled and unlabeled corpora simultaneously, using available class labels as an auxiliary signal to steer the representation toward task-relevant structure while still exploiting the full unlabeled collection for generalization.	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.	Label Propagation is a graph-based semi-supervised learning algorithm introduced by Zhu and Ghahramani in 2002 that spreads class labels from a small set of labeled nodes to a large set of unlabeled nodes by iteratively diffusing label information along the edges of a similarity graph, exploiting the manifold structure of the data.	Word2Vec is a neural word-embedding technique introduced by Mikolov and colleagues in 2013 that maps each word in a text corpus to a dense numeric vector. Words that appear in similar contexts end up close together in the vector space, so the embeddings capture semantic similarity that can be measured arithmetically.