পদ্ধতির তুলনা করুন

	শব্দার্থিক সাদৃশ্য	ডকুমেন্ট ক্লাস্টারিং	TF-IDF
ক্ষেত্র	টেক্সট খনন	টেক্সট খনন	টেক্সট খনন
পরিবার	Process / pipeline	Process / pipeline	Process / pipeline
উদ্ভবের বছর	2019	—	1988
প্রবর্তক	Nils Reimers & Iryna Gurevych (Sentence-BERT)	—	Salton & Buckley
ধরন	NLP text-comparison task	Unsupervised text-mining task	Text vectorization / term-weighting scheme
মৌলিক উৎস	Reimers, N. & Gurevych, I. (2019). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. EMNLP.	Aggarwal, C. C. & Zhai, C. (2012). Mining Text Data. Springer. ISBN: 9781461432227	Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523.
অপর নাম	semantic textual similarity, text similarity, Anlamsal Benzerlik Analizi	text clustering, unsupervised text grouping, Belge Kümeleme (Document Clustering)	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
সম্পর্কিত	4	4	3
সারসংক্ষেপ	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.	Document clustering is an unsupervised text-mining task that groups documents with similar content together without using any labels. It is used to organise large collections and for exploratory analysis, drawing on the body of text-mining techniques consolidated by Aggarwal and Zhai (2012) and compared empirically by Steinbach, Karypis and Kumar (2000).	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.