Linganisha mbinu

	Doc2Vec	GloVe Embeddings	Uainishaji wa Maandishi	TF-IDF
Nyanja	Uchimbaji wa Matini	Uchimbaji wa Matini	Uchimbaji wa Matini	Uchimbaji wa Matini
Familia	Process / pipeline	Process / pipeline	Process / pipeline	Process / pipeline
Mwaka wa asili	2014	2014	—	1988
Mwanzilishi	Quoc V. Le & Tomas Mikolov	Pennington, Socher & Manning	—	Salton & Buckley
Aina	Document-embedding representation learning	Static word-embedding model	Supervised NLP classification task	Text vectorization / term-weighting scheme
Chanzo asilia	Le, Q. V. & Mikolov, T. (2014). Distributed Representations of Sentences and Documents. Proceedings of the 31st International Conference on Machine Learning (ICML), 1188-1196.	Pennington, J., Socher, R. & Manning, C. D. (2014). GloVe: Global Vectors for Word Representation. EMNLP.	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.
Majina mbadala	paragraph vector, document embeddings, Doc2Vec Belge Gömülmeleri	GloVe, global vectors, GloVe Kelime Gömülmeleri	text categorization, document classification, topic classification, metin sınıflandırma	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
Zinazohusiana	4	3	4	3
Muhtasari	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.	GloVe (Global Vectors for Word Representation) is a static word-embedding model introduced by Pennington, Socher and Manning (2014) that learns word vectors directly from global word-word co-occurrence statistics gathered across an entire corpus. The resulting vectors place semantically related words close together and perform strongly on semantic analogy tasks.	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.