مقایسهٔ روش‌ها

	استخراج کلیدواژه	TF-IDF
حوزه	متن‌کاوی	متن‌کاوی
خانواده	Process / pipeline	Process / pipeline
سال پیدایش	—	1988
پدیدآور	—	Salton & Buckley
نوع	NLP text-mining task	Text vectorization / term-weighting scheme
منبع بنیادین	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.
نام‌های دیگر	keyphrase extraction, key term extraction, Anahtar Kelime Çıkarma (Keyword Extraction)	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
مرتبط	4	3
خلاصه	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.