Salīdzināt metodes

	Semantiskā līdzība	BERT Embeddings	Dokumentu kopu grupēšana	Sentimentu analīze
Nozare	Teksta ieguve	Teksta ieguve	Teksta ieguve	Teksta ieguve
Saime	Process / pipeline	Process / pipeline	Process / pipeline	Process / pipeline
Izcelsmes gads	2019	2019	—	—
Autors	Nils Reimers & Iryna Gurevych (Sentence-BERT)	Devlin, Chang, Lee & Toutanova (Google AI)	—	—
Tips	NLP text-comparison task	Contextual transformer text-representation method	Unsupervised text-mining task	NLP text-classification task
Pirmavots	Reimers, N. & Gurevych, I. (2019). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. EMNLP.	Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL-HLT, 4171-4186.	Aggarwal, C. C. & Zhai, C. (2012). Mining Text Data. Springer. ISBN: 9781461432227	Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.
Citi nosaukumi	semantic textual similarity, text similarity, Anlamsal Benzerlik Analizi	contextual embeddings, transformer embeddings, BERT Tabanlı Metin Gömülmeleri	text clustering, unsupervised text grouping, Belge Kümeleme (Document Clustering)	opinion mining, polarity detection, duygu analizi
Saistītās	4	4	4	3
Kopsavilkums	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.	BERT-based text embeddings, introduced by Devlin and colleagues at Google AI in 2019, turn text into context-sensitive dense vectors using a bidirectional Transformer encoder. Because the meaning of a word shifts with its context, BERT produces richer representations than static methods such as Word2Vec or topic models like LDA.	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).	Sentiment analysis, also called opinion mining, is a natural-language-processing task that detects the emotional tone of text — typically classifying it as positive, negative, or neutral. It turns unstructured opinion text into structured, quantifiable polarity signals using one of three families of approaches: sentiment lexicons, trained machine-learning classifiers, or pretrained transformer models.