Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Leksikālā daudzveidība× | TF-IDF× | |
|---|---|---|
| Nozare | Teksta ieguve | Teksta ieguve |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | — | 1988 |
| Autors≠ | — | Salton & Buckley |
| Tips≠ | Text quantification / lexical richness measurement | Text vectorization / term-weighting scheme |
| 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 ↗ | Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523. DOI ↗ |
| Citi nosaukumi | lexical richness, vocabulary richness, Sözcüksel Çeşitlilik Analizi | term weighting, tf-idf weighting, TF-IDF Vektörizasyonu |
| Saistītās | 3 | 3 |
| 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. | 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. |
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