Vertaile menetelmiä

	Doc2Vec – dokumenttien upotukset	Tekstinluokittelu
Tieteenala	Tekstinlouhinta	Tekstinlouhinta
Menetelmäperhe	Process / pipeline	Process / pipeline
Syntyvuosi	2014	—
Kehittäjä	Quoc V. Le & Tomas Mikolov	—
Tyyppi	Document-embedding representation learning	Supervised NLP classification task
Alkuperäislähde	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.
Rinnakkaisnimet	paragraph vector, document embeddings, Doc2Vec Belge Gömülmeleri	text categorization, document classification, topic classification, metin sınıflandırma
Liittyvät	4	4
Tiivistelmä	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.