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| Езиков модел N-грама× | TF-IDF× | |
|---|---|---|
| Област | Извличане на текст | Извличане на текст |
| Семейство | Process / pipeline | Process / pipeline |
| Година на възникване≠ | — | 1988 |
| Създател≠ | — | Salton & Buckley |
| Тип≠ | Statistical language model | Text vectorization / term-weighting scheme |
| Основополагащ източник≠ | 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 ↗ |
| Други названия | n-gram model, statistical language model, N-gram Dil Modeli | term weighting, tf-idf weighting, TF-IDF Vektörizasyonu |
| Свързани≠ | 4 | 3 |
| Резюме≠ | 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. |
| ScholarGateНабор от данни ↗ |
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