Vertaile menetelmiä

	Semanttinen samankaltaisuus – Merkityksen mittaaminen tekstien välillä	TF-IDF
Tieteenala	Tekstinlouhinta	Tekstinlouhinta
Menetelmäperhe	Process / pipeline	Process / pipeline
Syntyvuosi	2019	1988
Kehittäjä	Nils Reimers & Iryna Gurevych (Sentence-BERT)	Salton & Buckley
Tyyppi	NLP text-comparison task	Text vectorization / term-weighting scheme
Alkuperäislähde	Reimers, N. & Gurevych, I. (2019). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. EMNLP.	Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523.
Rinnakkaisnimet	semantic textual similarity, text similarity, Anlamsal Benzerlik Analizi	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
Liittyvät	4	3
Tiivistelmä	Semantic similarity analysis measures how close in meaning two texts are, rather than how many words they share on the surface. Building on the Sentence-BERT work of Reimers and Gurevych (2019), it represents each text as a vector and compares those vectors so that paraphrases score high even when their wording differs.	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.