Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Leksikālā daudzveidība× | Tēmu modelēšana× | |
|---|---|---|
| Nozare≠ | Teksta ieguve | Dziļā mācīšanās |
| Saime≠ | Process / pipeline | Machine learning |
| Izcelsmes gads≠ | — | 1999–2003 |
| Autors≠ | — | Hofmann, T. (pLSA, 1999); Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA, 2003) |
| Tips≠ | Text quantification / lexical richness measurement | Unsupervised generative probabilistic model |
| Pirmavots≠ | 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. DOI ↗ | Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022. link ↗ |
| Citi nosaukumi≠ | lexical richness, vocabulary richness, Sözcüksel Çeşitlilik Analizi | Latent Semantic Analysis, probabilistic topic modeling, topic discovery, thematic modeling |
| Saistītās≠ | 3 | 5 |
| Kopsavilkums≠ | 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. |
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