Võrdle meetodeid

	Morfoloogiline analüüs	Keel(t)e tuvastamine (LID)	TF-IDF
Valdkond	Tekstikaeve	Tekstikaeve	Tekstikaeve
Perekond	Process / pipeline	Process / pipeline	Process / pipeline
Tekkeaasta	1980	—	1988
Looja	M.F. Porter (Porter stemmer)	—	Salton & Buckley
Tüüp	Text-normalisation preprocessing task	NLP text-classification task	Text vectorization / term-weighting scheme
Algallikas	Porter, M.F. (1980). An Algorithm for Suffix Stripping. Program, 14(3), 130-137.	Lui, M. & Baldwin, T. (2012). langid.py: An Off-the-shelf Language Identification Tool. Proceedings of the ACL 2012 System Demonstrations.	Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523.
Rööpnimetused	stemming, lemmatization, Morfolojik Analiz ve Kök Bulma	language detection, LID, Dil Tanımlama (Language Identification)	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
Seotud	4	4	3
Kokkuvõte	Morphological analysis splits words into their stems and affixes so that different surface forms of the same word can be treated as one. It covers two complementary approaches — rule-based stemming, such as the Porter (1980) and Snowball algorithms, and dictionary-aware lemmatization — and is a critical text-normalisation step for agglutinative languages such as Turkish and Arabic.	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.	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.