Linganisha mbinu

	Uainishaji wa Maandishi	Uundaji wa Mada
Nyanja	Uchimbaji wa Matini	Ujifunzaji wa Kina
Familia	Process / pipeline	Machine learning
Mwaka wa asili	—	1999–2003
Mwanzilishi	—	Hofmann, T. (pLSA, 1999); Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA, 2003)
Aina	Supervised NLP classification task	Unsupervised generative probabilistic model
Chanzo asilia	Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer.	Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.
Majina mbadala	text categorization, document classification, topic classification, metin sınıflandırma	Latent Semantic Analysis, probabilistic topic modeling, topic discovery, thematic modeling
Zinazohusiana	4	5
Muhtasari	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.	Topic Modeling is a family of unsupervised probabilistic techniques for discovering latent thematic structure in large text collections. By learning which words tend to co-occur, models such as Latent Dirichlet Allocation (LDA) automatically surface coherent topics — each represented as a distribution over vocabulary — without requiring labelled data.