Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Лексикално разнообразие× | Тематично моделиране× | |
|---|---|---|
| Област≠ | Извличане на текст | Дълбоко обучение |
| Семейство≠ | Process / pipeline | Machine learning |
| Година на възникване≠ | — | 1999–2003 |
| Създател≠ | — | Hofmann, T. (pLSA, 1999); Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA, 2003) |
| Тип≠ | Text quantification / lexical richness measurement | Unsupervised generative probabilistic model |
| Основополагащ източник≠ | 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 ↗ |
| Други названия≠ | lexical richness, vocabulary richness, Sözcüksel Çeşitlilik Analizi | Latent Semantic Analysis, probabilistic topic modeling, topic discovery, thematic modeling |
| Свързани≠ | 3 | 5 |
| Резюме≠ | 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. |
| ScholarGateНабор от данни ↗ |
|
|