방법 비교

	n-gram 언어 모델	TF-IDF
분야	텍스트 마이닝	텍스트 마이닝
계열	Process / pipeline	Process / pipeline
기원 연도	—	1988
창시자	—	Salton & Buckley
유형	Statistical language model	Text vectorization / term-weighting scheme
원전	Jurafsky, D. & Martin, J.H. (2023). Speech and Language Processing, 3rd ed.	Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523.
별칭	n-gram model, statistical language model, N-gram Dil Modeli	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
관련	4	3
요약	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.	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.