Comparer des méthodes

	Le regroupement de documents	Extraction de mots-clés	Similarité sémantique
Domaine	Fouille de textes	Fouille de textes	Fouille de textes
Famille	Process / pipeline	Process / pipeline	Process / pipeline
Année d'origine	—	—	2019
Auteur d'origine	—	—	Nils Reimers & Iryna Gurevych (Sentence-BERT)
Type	Unsupervised text-mining task	NLP text-mining task	NLP text-comparison task
Source fondatrice	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.	Reimers, N. & Gurevych, I. (2019). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. EMNLP.
Alias	text clustering, unsupervised text grouping, Belge Kümeleme (Document Clustering)	keyphrase extraction, key term extraction, Anahtar Kelime Çıkarma (Keyword Extraction)	semantic textual similarity, text similarity, Anlamsal Benzerlik Analizi
Apparentées	4	4	4
Résumé	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).	Semantic similarity analysis measures how close in meaning two texts are, rather than how many words they share on the surface. Building on the Sentence-BERT work of Reimers and Gurevych (2019), it represents each text as a vector and compares those vectors so that paraphrases score high even when their wording differs.