השוואת שיטות

	חילוץ מילות מפתח	TF-IDF
תחום	כריית טקסט	כריית טקסט
משפחה	Process / pipeline	Process / pipeline
שנת המקור	—	1988
הוגה השיטה	—	Salton & Buckley
סוג	NLP text-mining task	Text vectorization / term-weighting scheme
מקור מכונן	Mihalcea, R. & Tarau, P. (2004). TextRank: Bringing Order into Texts. EMNLP, 404-411.	Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523.
כינויים	keyphrase extraction, key term extraction, Anahtar Kelime Çıkarma (Keyword Extraction)	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
קשורות	4	3
תקציר	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).	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.