Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Identification de la langue (LID)× | Vérification orthographique et grammaticale× | |
|---|---|---|
| Domaine | Fouille de textes | Fouille de textes |
| Famille | Process / pipeline | Process / pipeline |
| Année d'origine≠ | — | 2003 |
| Auteur d'origine≠ | — | Daniel Naber (rule-based checker); Peter Norvig (statistical spelling correction) |
| Type≠ | NLP text-classification task | Text-mining preprocessing / quality-assessment task |
| Source fondatrice≠ | Lui, M. & Baldwin, T. (2012). langid.py: An Off-the-shelf Language Identification Tool. Proceedings of the ACL 2012 System Demonstrations. link ↗ | Naber, D. (2003). A Rule-Based Style and Grammar Checker. Diploma Thesis. link ↗ |
| Alias≠ | language detection, LID, Dil Tanımlama (Language Identification) | spell checking, grammar checking, text proofing, Yazım ve Dilbilgisi Denetimi |
| Apparentées | 4 | 4 |
| Résumé≠ | Language identification is a natural-language-processing task that automatically detects which language a piece of text is written in. Building on off-the-shelf tools such as langid.py (Lui & Baldwin, 2012) and the efficient classifiers of Joulin et al. (2017), it is widely used to preprocess and filter multilingual data sets. | 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. |
| ScholarGateJeu de données ↗ |
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