Sammenlign metoder
Gjennomgå de valgte metodene side om side; rader som avviker, er uthevet.
| N-gram språkmodell× | TF-IDF× | |
|---|---|---|
| Fagfelt | Tekstutvinning | Tekstutvinning |
| Familie | Process / pipeline | Process / pipeline |
| Opprinnelsesår≠ | — | 1988 |
| Opphavsperson≠ | — | Salton & Buckley |
| Type≠ | Statistical language model | Text vectorization / term-weighting scheme |
| Opprinnelig kilde≠ | Jurafsky, D. & Martin, J.H. (2023). Speech and Language Processing, 3rd ed. link ↗ | Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523. DOI ↗ |
| Alias | n-gram model, statistical language model, N-gram Dil Modeli | term weighting, tf-idf weighting, TF-IDF Vektörizasyonu |
| Relaterte≠ | 4 | 3 |
| Sammendrag≠ | An n-gram language model is a statistical model that predicts the probability of the next word by looking only at the previous n−1 words. Described in detail by Jurafsky and Martin (Speech and Language Processing), it provides foundational infrastructure for text generation, spelling correction, and speech recognition. | 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. |
| ScholarGateDatasett ↗ |
|
|