Porovnat metody

	Klasifikace textu	Extrakce klíčových slov
Obor	Dolování textu	Dolování textu
Rodina	Process / pipeline	Process / pipeline
Rok vzniku	—	—
Tvůrce	—	—
Typ	Supervised NLP classification task	NLP text-mining task
Původní zdroj	Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer.	Mihalcea, R. & Tarau, P. (2004). TextRank: Bringing Order into Texts. EMNLP, 404-411.
Další názvy	text categorization, document classification, topic classification, metin sınıflandırma	keyphrase extraction, key term extraction, Anahtar Kelime Çıkarma (Keyword Extraction)
Příbuzné	4	4
Shrnutí	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.	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).