Uporedite metode

	Klasifikacija teksta	Grupisanje dokumenata	Ekstrakcija ključnih reči
Oblast	Rudarenje teksta	Rudarenje teksta	Rudarenje teksta
Porodica	Process / pipeline	Process / pipeline	Process / pipeline
Godina nastanka	—	—	—
Tvorac	—	—	—
Tip	Supervised NLP classification task	Unsupervised text-mining task	NLP text-mining task
Temeljni izvor	Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer.	Aggarwal, C. C. & Zhai, C. (2012). Mining Text Data. Springer. ISBN: 9781461432227	Mihalcea, R. & Tarau, P. (2004). TextRank: Bringing Order into Texts. EMNLP, 404-411.
Drugi nazivi	text categorization, document classification, topic classification, metin sınıflandırma	text clustering, unsupervised text grouping, Belge Kümeleme (Document Clustering)	keyphrase extraction, key term extraction, Anahtar Kelime Çıkarma (Keyword Extraction)
Srodne	4	4	4
Sažetak	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.	Document clustering is an unsupervised text-mining task that groups documents with similar content together without using any labels. It is used to organise large collections and for exploratory analysis, drawing on the body of text-mining techniques consolidated by Aggarwal and Zhai (2012) and compared empirically by Steinbach, Karypis and Kumar (2000).	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).