Sammenlign metoder
Gjennomgå de valgte metodene side om side; rader som avviker, er uthevet.
| Korrekturlesing× | N-gram språkmodell× | |
|---|---|---|
| Fagfelt | Tekstutvinning | Tekstutvinning |
| Familie | Process / pipeline | Process / pipeline |
| Opprinnelsesår≠ | 2003 | — |
| Opphavsperson≠ | Daniel Naber (rule-based checker); Peter Norvig (statistical spelling correction) | — |
| Type≠ | Text-mining preprocessing / quality-assessment task | Statistical language model |
| Opprinnelig kilde≠ | Naber, D. (2003). A Rule-Based Style and Grammar Checker. Diploma Thesis. link ↗ | Jurafsky, D. & Martin, J.H. (2023). Speech and Language Processing, 3rd ed. link ↗ |
| Alias≠ | spell checking, grammar checking, text proofing, Yazım ve Dilbilgisi Denetimi | n-gram model, statistical language model, N-gram Dil Modeli |
| Relaterte | 4 | 4 |
| Sammendrag≠ | Spelling and grammar checking is a text-mining task that detects spelling mistakes and grammatical errors in text and proposes corrections. Building on Naber's rule-based style and grammar checker (2003) and Norvig's statistical spelling corrector (2009), it is used for data-quality assessment and text normalisation before further analysis. | 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. |
| ScholarGateDatasett ↗ |
|
|