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| Detektion av hatretorik× | BERT-inbäddningar× | |
|---|---|---|
| Ämnesområde | Textutvinning | Textutvinning |
| Familj | Process / pipeline | Process / pipeline |
| Ursprungsår≠ | — | 2019 |
| Upphovsperson≠ | — | Devlin, Chang, Lee & Toutanova (Google AI) |
| Typ≠ | NLP text-classification task | Contextual transformer text-representation method |
| Ursprungskälla≠ | Davidson, T., Warmsley, D., Macy, M. & Weber, I. (2017). Automated Hate Speech Detection and the Problem of Offensive Language. ICWSM, 11(1), 512-515. DOI ↗ | Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL-HLT, 4171-4186. DOI ↗ |
| Alias | offensive language detection, toxic content detection, Nefret Söylemi Tespiti | contextual embeddings, transformer embeddings, BERT Tabanlı Metin Gömülmeleri |
| Närliggande | 4 | 4 |
| Sammanfattning≠ | 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. |
| ScholarGateDatamängd ↗ |
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