Vertaile menetelmiä

	Tekstin deduplikointi	TF-IDF
Tieteenala	Tekstinlouhinta	Tekstinlouhinta
Menetelmäperhe	Process / pipeline	Process / pipeline
Syntyvuosi	1997	1988
Kehittäjä	Andrei Z. Broder (MinHash / Resemblance theory, 1997)	Salton & Buckley
Tyyppi	Text preprocessing / corpus quality pipeline	Text vectorization / term-weighting scheme
Alkuperäislähde	Broder, A.Z. (1997). On the Resemblance and Containment of Documents. Compression and Complexity of SEQUENCES.	Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523.
Rinnakkaisnimet	near-duplicate detection, document deduplication, corpus deduplication, Metin Tekilleştirme (Near-Duplicate Detection)	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
Liittyvät	5	3
Tiivistelmä	Text deduplication is a corpus-quality pipeline that identifies and removes exact and near-duplicate documents from large text collections. Grounded in Andrei Broder's 1997 resemblance theory, it is widely used to improve dataset quality for machine learning model training, search engine indexing, and any downstream NLP task that assumes a non-redundant corpus.	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.