Comparer des méthodes

	Modèle de langage N-gramme	Classification de texte
Domaine	Fouille de textes	Fouille de textes
Famille	Process / pipeline	Process / pipeline
Année d'origine	—	—
Auteur d'origine	—	—
Type	Statistical language model	Supervised NLP classification task
Source fondatrice	Jurafsky, D. & Martin, J.H. (2023). Speech and Language Processing, 3rd ed.	Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer.
Alias	n-gram model, statistical language model, N-gram Dil Modeli	text categorization, document classification, topic classification, metin sınıflandırma
Apparentées	4	4
Résumé	An n-gram language model is a statistical model that predicts the probability of the next word by looking only at the previous n−1 words. Described in detail by Jurafsky and Martin (Speech and Language Processing), it provides foundational infrastructure for text generation, spelling correction, and speech recognition.	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.