Porównaj metody

	Doc2Vec	Klasyfikacja Tekstu	TF-IDF
Dziedzina	Eksploracja tekstu	Eksploracja tekstu	Eksploracja tekstu
Rodzina	Process / pipeline	Process / pipeline	Process / pipeline
Rok powstania	2014	—	1988
Twórca	Quoc V. Le & Tomas Mikolov	—	Salton & Buckley
Typ	Document-embedding representation learning	Supervised NLP classification task	Text vectorization / term-weighting scheme
Źródło pierwotne	Le, Q. V. & Mikolov, T. (2014). Distributed Representations of Sentences and Documents. Proceedings of the 31st International Conference on Machine Learning (ICML), 1188-1196.	Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer.	Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523.
Inne nazwy	paragraph vector, document embeddings, Doc2Vec Belge Gömülmeleri	text categorization, document classification, topic classification, metin sınıflandırma	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
Pokrewne	4	4	3
Podsumowanie	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.	Text classification, also called text categorization, is a supervised natural-language-processing task that automatically assigns documents to predefined categories. Building on the support-vector-machine approach to text categorization established by Joachims (1998) and consolidated in the text-mining literature by Aggarwal and Zhai (2012), it powers tasks such as spam detection and topic classification by learning from labelled examples.	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.