Usporedite metode

	Ekstrakcija ključnih riječi	Modeliranje tema
Područje	Rudarenje teksta	Duboko učenje
Obitelj	Process / pipeline	Machine learning
Godina nastanka	—	1999–2003
Tvorac	—	Hofmann, T. (pLSA, 1999); Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA, 2003)
Vrsta	NLP text-mining task	Unsupervised generative probabilistic model
Temeljni izvor	Mihalcea, R. & Tarau, P. (2004). TextRank: Bringing Order into Texts. EMNLP, 404-411.	Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.
Drugi nazivi	keyphrase extraction, key term extraction, Anahtar Kelime Çıkarma (Keyword Extraction)	Latent Semantic Analysis, probabilistic topic modeling, topic discovery, thematic modeling
Srodne	4	5
Sažetak	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).	Topic Modeling is a family of unsupervised probabilistic techniques for discovering latent thematic structure in large text collections. By learning which words tend to co-occur, models such as Latent Dirichlet Allocation (LDA) automatically surface coherent topics — each represented as a distribution over vocabulary — without requiring labelled data.