Jämför metoder

	Språkidentifiering (LID)	N-gram språkmodell	TF-IDF
Ämnesområde	Textutvinning	Textutvinning	Textutvinning
Familj	Process / pipeline	Process / pipeline	Process / pipeline
Ursprungsår	—	—	1988
Upphovsperson	—	—	Salton & Buckley
Typ	NLP text-classification task	Statistical language model	Text vectorization / term-weighting scheme
Ursprungskälla	Lui, M. & Baldwin, T. (2012). langid.py: An Off-the-shelf Language Identification Tool. Proceedings of the ACL 2012 System Demonstrations.	Jurafsky, D. & Martin, J.H. (2023). Speech and Language Processing, 3rd ed.	Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523.
Alias	language detection, LID, Dil Tanımlama (Language Identification)	n-gram model, statistical language model, N-gram Dil Modeli	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
Närliggande	4	4	3
Sammanfattning	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.	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.