Porovnat metody

	BERT Embeddings	Rozpoznávání pojmenovaných entit (NER)
Obor	Dolování textu	Dolování textu
Rodina	Process / pipeline	Process / pipeline
Rok vzniku	2019	—
Tvůrce	Devlin, Chang, Lee & Toutanova (Google AI)	—
Typ	Contextual transformer text-representation method	NLP sequence-labelling task
Původní zdroj	Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL-HLT, 4171-4186.	Nadeau, D. & Sekine, S. (2007). A survey of named entity recognition. Lingvisticae Investigationes.
Další názvy	contextual embeddings, transformer embeddings, BERT Tabanlı Metin Gömülmeleri	NER, entity tagging, Adlandırılmış Varlık Tanıma (NER)
Příbuzné	4	3
Shrnutí	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.	Named entity recognition (NER) is a natural-language-processing task that automatically detects and labels entities in text — such as people, organisations, locations, and dates. Surveyed by Nadeau and Sekine (2007) and later advanced with neural architectures by Lample et al. (2016), it turns free-running text into tagged spans that downstream tools can use.