Porovnat metody

	Detekce nenávistných projevů	BERT Embeddings
Obor	Dolování textu	Dolování textu
Rodina	Process / pipeline	Process / pipeline
Rok vzniku	—	2019
Tvůrce	—	Devlin, Chang, Lee & Toutanova (Google AI)
Typ	NLP text-classification task	Contextual transformer text-representation method
Původní zdroj	Davidson, T., Warmsley, D., Macy, M. & Weber, I. (2017). Automated Hate Speech Detection and the Problem of Offensive Language. ICWSM, 11(1), 512-515.	Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL-HLT, 4171-4186.
Další názvy	offensive language detection, toxic content detection, Nefret Söylemi Tespiti	contextual embeddings, transformer embeddings, BERT Tabanlı Metin Gömülmeleri
Příbuzné	4	4
Shrnutí	Hate speech detection is a natural-language-processing task that automatically identifies hateful, offensive, or harmful text on social media and online platforms. The task was sharpened by Davidson and colleagues (2017), who showed why separating genuine hate speech from merely offensive language is a hard, distinct classification problem rather than a single toxicity score.	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.