Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Automātiska teksta korekcija× | N-gramu valodu modelis× | |
|---|---|---|
| Nozare | Teksta ieguve | Teksta ieguve |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 2003 | — |
| Autors≠ | Daniel Naber (rule-based checker); Peter Norvig (statistical spelling correction) | — |
| Tips≠ | Text-mining preprocessing / quality-assessment task | Statistical language model |
| Pirmavots≠ | 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 ↗ |
| Citi nosaukumi≠ | spell checking, grammar checking, text proofing, Yazım ve Dilbilgisi Denetimi | n-gram model, statistical language model, N-gram Dil Modeli |
| Saistītās | 4 | 4 |
| Kopsavilkums≠ | 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. |
| ScholarGateDatu kopa ↗ |
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