Compară metode

	Diversitatea Lexicală	Modelarea tematică
Domeniu	Mineritul textelor	Învățare profundă
Familie	Process / pipeline	Machine learning
Anul apariției	—	1999–2003
Autorul original	—	Hofmann, T. (pLSA, 1999); Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA, 2003)
Tip	Text quantification / lexical richness measurement	Unsupervised generative probabilistic model
Sursa seminală	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.	Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.
Denumiri alternative	lexical richness, vocabulary richness, Sözcüksel Çeşitlilik Analizi	Latent Semantic Analysis, probabilistic topic modeling, topic discovery, thematic modeling
Înrudite	3	5
Rezumat	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.	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.