विधियों की तुलना करें

	कीवर्ड निष्कर्षण	NMF विषय मॉडलिंग	TF-IDF
क्षेत्र	पाठ खनन	पाठ खनन	पाठ खनन
परिवार	Process / pipeline	Process / pipeline	Process / pipeline
उद्भव वर्ष	—	1999	1988
प्रवर्तक	—	Lee & Seung	Salton & Buckley
प्रकार	NLP text-mining task	Matrix-factorization topic model	Text vectorization / term-weighting scheme
मौलिक स्रोत	Mihalcea, R. & Tarau, P. (2004). TextRank: Bringing Order into Texts. EMNLP, 404-411.	Lee, D.D. & Seung, H.S. (1999). Learning the Parts of Objects by Non-negative Matrix Factorization. Nature, 401, 788-791.	Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523.
उपनाम	keyphrase extraction, key term extraction, Anahtar Kelime Çıkarma (Keyword Extraction)	non-negative matrix factorization topic modeling, NMF topics, Konu Modelleme — NMF	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
संबंधित	4	4	3
सारांश	Keyword extraction is a natural-language-processing task that automatically identifies the words or phrases that best represent the content of a document. It turns a body of free text into a compact, ranked list of key terms, drawing on statistical, graph-based methods such as TextRank (Mihalcea & Tarau, 2004), or embedding-based methods such as KeyBERT (Grootendorst, 2020).	NMF topic modeling uses Non-negative Matrix Factorization — the parts-based decomposition introduced by Lee and Seung (1999) — to extract document-topic distributions from a corpus. By factoring a document-term matrix into two non-negative matrices, it recovers a small set of topics and tends to produce more interpretable topics than LDA.	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.