Comparer des méthodes

	Analyse de collocations	Diversité lexicale	Analyse de fréquence textuelle
Domaine	Fouille de textes	Fouille de textes	Fouille de textes
Famille	Process / pipeline	Process / pipeline	Process / pipeline
Année d'origine	1990	—	1949
Auteur d'origine	Church & Hanks	—	George K. Zipf (frequency-distribution foundation)
Type	Statistical text-mining technique	Text quantification / lexical richness measurement	Descriptive text-mining analysis
Source fondatrice	Church, K.W. & Hanks, P. (1990). Word Association Norms, Mutual Information, and Lexicography. Computational Linguistics, 16(1), 22-29.	McCarthy, P. M. & Jarvis, S. (2010). MTLD, vocd-D, and HD-D: A validation study of sophisticated approaches to lexical diversity assessment. Behavior Research Methods, 42(2), 381-392.	Zipf, G. K. (1949). Human Behavior and the Principle of Least Effort. Addison-Wesley.
Alias	word association, collocation extraction, Birliktelik Analizi (Collocation Analysis)	lexical richness, vocabulary richness, Sözcüksel Çeşitlilik Analizi	word frequency analysis, n-gram frequency analysis, Metin Frekans Analizi
Apparentées	3	3	4
Résumé	Collocation analysis is a statistical text-mining technique that identifies word pairs or expressions that frequently occur together, using association measures rather than chance co-occurrence. Introduced in the lexicography work of Church and Hanks (1990), it is used for terminology extraction and language analysis, surfacing the multi-word units that carry meaning in a corpus.	Lexical diversity analysis quantifies how varied the vocabulary of a text is — how rich an author's word choice is — using measures such as the type-token ratio (TTR), MTLD, vocd-D, and Yule's K. The MTLD and vocd-D measures were validated by McCarthy and Jarvis (2010), building on earlier work by Tweedie and Baayen (1998) on the stability of lexical-richness measures.	Text frequency analysis is a descriptive text-mining method that counts how often words, n-grams, and phrases occur in a corpus to reveal content patterns and dominant themes. It rests on the frequency-distribution insight formalised by George K. Zipf (1949), that a few terms occur very often while most are rare, and it is one of the most basic and widely used entry points into quantitative text analysis.