Порівняння методів

	Лексична різноманітність	TF-IDF	Тематичне моделювання
Галузь	Інтелектуальний аналіз тексту	Інтелектуальний аналіз тексту	Глибоке навчання
Родина	Process / pipeline	Process / pipeline	Machine learning
Рік появи	—	1988	1999–2003
Автор методу	—	Salton & Buckley	Hofmann, T. (pLSA, 1999); Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA, 2003)
Тип	Text quantification / lexical richness measurement	Text vectorization / term-weighting scheme	Unsupervised generative probabilistic model
Основоположне джерело	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.	Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523.	Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.
Інші назви	lexical richness, vocabulary richness, Sözcüksel Çeşitlilik Analizi	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu	Latent Semantic Analysis, probabilistic topic modeling, topic discovery, thematic modeling
Пов'язані	3	3	5
Підсумок	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.	TF-IDF, introduced by Salton and Buckley (1988), is a term-weighting scheme that scores each word in a document by how often it appears there and how rare it is across the whole collection. It turns raw text into weighted document vectors, giving high weight to terms that are frequent in one document but uncommon elsewhere.	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.